Data for AI 2020 Conference

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September 14, 2020
  - September 18, 2020
  • About
  • Agenda
  • Sessions
  • Speakers
  • Sponsors & Partners
  • FAQ

Have Data and AI Needs? This conference is for you!

Data for AI Week: Virtual Conference Experience – Addressing the Data Side of AI in a Conference Unlike any Other

The Data For AI Conference is an online event unlike any you’ve attended. The online event combines a large library of on-demand content with live keynotes (originally live Sep. 14-18, 2020) and live webinar-style panel engagements, “ask-me-anything” style expert sessions, and educational content. Content is meant to be consumed around your schedule, not a predetermined schedule created for you. Key topics include Data Engineering, Data Preparation, Data Labeling & Annotation, Sourcing Data and Data Generation for AI.

Five Topics:

  • Data Engineering
  • Data Preparation
  • Data Labeling & Annotation
  • Sourcing and Generating Data
  • All Other Topics Data-Related for AI

Three Tracks:

  • Industry Applications
  • Government and Public Sector Sessions
  • Technology Deep Dives

All times listed are US Eastern Time Zone (ET)

  • On Demand


  • For this dashboard we dug into an April 25th, 2020 Washington Post article about COVID-19 hotspots at US meat processing plants. Using data from our partners at X-Mode and Safegraph, we analyze activity at these plants as well as the COVID-19 infections in nearby communities. Finally, we trace activity from one plant to identify potential virus hotspots in nearby locations. X-Mode’s dataset used in this research is aggregated and generalized. X-Mode does not collect or share any personally identifiable information such as name, email, or phone number. All devices have given consent to location collection.
    View Session Details

 

Keynote

AI Experience at the VA

Wed. September 16, 2020 @ 13:00 ET

AI is enabling a new paradigm. By leveraging voluminous real-time information and new algorithms, there is a promise of better and more efficient care. In this session, several case studies from the work of the National Artificial Intelligence Institute at the VA and collaborations will be discussed, including research and development that empowers Veterans to search for clinical trials and physicians to evaluate COVID-19-associated prognosis and needs.

Day 3, Data Engineering, General Sessions,
Government

AI Experience at the VA

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On Demand

AI Demo Showcase: DataRobot

On Demand

Watch the demo to see how DataRobot’s automated machine learning platform combines predictive modeling expertise with the best practices of data science to deliver accurate and actionable predictions with full transparency and interoperability.

Data Engineering, Data Preparation, Demo Showcase, On-Demand AI Experiences,
Technology

AI Demo Showcase: DataRobot

View Session
On Demand

AI Demo Showcase: Labelbox

On Demand

In this demo, we'll walk you through the Labelbox platform and show you how quickly you can create projects and start annotating high-quality training data.

Data Labeling, Data Preparation, Demo Showcase, On-Demand AI Experiences,
Technology

AI Demo Showcase: Labelbox

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On Demand

AI Demo Showcase: Veritone

On Demand

Watch the demo to see a real-time demonstration of our AI Powered transcription, redaction, and facial recognition capabilities.

Data Engineering, Data Preparation, Demo Showcase, General Sessions, On-Demand AI Experiences,
GovernmentTechnology

AI Demo Showcase: Veritone

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On Demand

AI Demo Showcase: OmniSci

On Demand

See how OmniSci and our partners at AWS, Safegraph, Veraset, and X-Mode are using anonymized, data-driven methods to contribute to relief efforts at a national scale for the next phase of the COVID-19 response efforts.

Data Engineering, Demo Showcase, General Sessions, On-Demand AI Experiences,
GovernmentTechnology

AI Demo Showcase: OmniSci

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On Demand

AI Demo Showcase: Alegion

On Demand

We demonstrate the Alegion platform's capability to support 4K video annotation and accelerate high-quality labeling through automation and machine learning.

Data Engineering, Data Labeling, Demo Showcase, On-Demand AI Experiences,
Technology

AI Demo Showcase: Alegion

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Live

Ask an Expert Session: Veritone

Fri. September 18, 2020 @ 14:00 ET

Join us for an open Ask an Expert Session with experts from Veritone

Day 5, Data Engineering, Data Preparation, Expert Sessions,
GovernmentTechnology

Ask an Expert Session: Veritone

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Live

Ask an Expert Session: Labelbox

Fri. September 18, 2020 @ 12:00 ET

Join us for an open Ask an Expert Session with experts from Labelbox

Day 5, Data Labeling, Data Preparation, Expert Sessions,
Technology

Ask an Expert Session: Labelbox

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Live

Ask an Expert Session: Amazon Web Services (AWS)

Tue. September 15, 2020 @ 12:00 ET

Join us for an open Ask an Expert Session with experts from Amazon Web Services (AWS)

Day 2, Data Engineering, Data Labeling, Data Preparation, Expert Sessions,
GovernmentIndustryTechnology

Ask an Expert Session: Amazon Web Services (AWS)

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Live

Ask an Expert Session: Alegion

Wed. September 16, 2020 @ 15:00 ET

Join us for an open Ask an Expert Session with experts from Alegion

Day 3, Data Engineering, Data Labeling, Expert Sessions,
Technology

Ask an Expert Session: Alegion

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On Demand

Getting Started with AI in the Public Sector – ML Use Cases

On Demand

This interactive demo will cover popular ML use cases in the Government. You’ll leave with a roadmap towards implementing AI and ML at your agency, allowing you to find insights and value in your data that you never thought possible.

Data Engineering, General Sessions,
Government

Getting Started with AI in the Public Sector – ML Use Cases

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On Demand

Getting Started with AI in the Public Sector

On Demand

In this session, we’ll provide an introduction to machine learning, discuss key use cases in the Public Sector and explore how big data technologies have evolved to enable machine learning to produce more accurate predictions and unlock insights buried in your data.

General Sessions
Government

Getting Started with AI in the Public Sector

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On Demand

Improving Public Health Surveillance During COVID-19 with Data Analytics and AI

On Demand

With these fundamental data problems solved, health organizations can refocus their efforts on building analytics and ML products instead of wrangling their data. One example is the COVID-19 surveillance solution developed on top of Databricks, which is being deployed in a number of state and local government health departments, as well as by a number of hospitals and care facilities across the U.S.

Data Engineering, General Sessions,
GovernmentIndustry

Improving Public Health Surveillance During COVID-19 with Data Analytics and AI

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On Demand

Detecting Financial Fraud at Scale

On Demand

Using machine learning it is possible to develop intelligent patterns that detect fraudulent behavior while complying with regulatory rules (e.g. GDPR). Databricks helps make this process simple, reliable, scalable and reproducible. With Databricks, organizations can build a modular solution that evolves as the fraudulent behavior patterns do.

General Sessions
Industry

Detecting Financial Fraud at Scale

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On Demand

Imagery Analysis and Detection

On Demand

This demo will demonstrate how to detect available parking spots from imagery using Deep Learning.

General Sessions, Sourcing Data,
Technology

Imagery Analysis and Detection

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On Demand

Opioid Epidemic: Data Mining and Modeling Pharmacy Anomalies

On Demand

We leverage Databricks to ingest pharma transaction data and red flag anomalous opioid distributions.

Data Engineering, General Sessions,
Industry

Opioid Epidemic: Data Mining and Modeling Pharmacy Anomalies

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On Demand

Real Time Threat Detection

On Demand

Since 2013, cybercriminals have stolen over 14.7B digital records from organizations across the industry. Responding quickly to threats is critical to avoiding a breach. To do this successfully, the private sector and government organizations need to monitor and analyze billions of data signals in real-time and perform ad-hoc analysis over large time windows of historical data. Yet, existing security tools are struggling to keep up. Overcoming these challenges requires a new approach to threat detection rooted in big data, analytics and AI.

Data Engineering, Data Preparation, General Sessions,
Industry

Real Time Threat Detection

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On Demand

Predictive Analytics in a Real Time World

On Demand

We will describe how a data scientist, data engineer, and analyst all work together on a real time world use case.

Data Engineering, General Sessions,
Technology

Predictive Analytics in a Real Time World

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On Demand

Build and Manage Your Data Lake with Delta Lake

On Demand

Learn about the features of Delta Lake that allow it to embody the best implementation of a lakehouse architecture, including tables that can handle both streaming and batch data, ACID transactions, schema enforcement, and evolution, time travel, and updates, inserts, and deletes.

Data Engineering, Data Preparation, On-Demand AI Experiences,
Technology

Build and Manage Your Data Lake with Delta Lake

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On Demand

Intro to MLflow

On Demand

Discover the new MLflow Model Registry, which simplifies the model productionization process by helping data teams stage, test, deploy, and monitor ML models through a simple, collaborative interface.

Data Engineering, Data Preparation,
Technology

Intro to MLflow

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On Demand

Detecting and Investigating Tactics of State Sponsored Espionage and Cyber Criminals

On Demand

In this demo, you will learn how you can detect nation state attackers like APT19 and cybercriminals like the Turla gang. We bring in terabytes of data from endpoints and DNS logs, enrich them with threat intel and run machine learning models to detect threat activities.

General Sessions
Government

Detecting and Investigating Tactics of State Sponsored Espionage and Cyber Criminals

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On Demand

Intro to Databricks

On Demand

Learn about the different parts of the Databricks Unified Data Analytics platform, and how they work together in this overview. We'll cover some of the main data analytics use cases, and show you how Databricks simplifies the workflow

Data Engineering, Data Preparation,
Technology

Intro to Databricks

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On Demand

The State of Data Governance in the Public Sector

On Demand

The State of Data Governance in the Public Sector

Data Engineering, On-Demand AI Experiences,
Government

The State of Data Governance in the Public Sector

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Live

Bring Your Document Archives to Life with Azure Cognitive Search

Fri. September 18, 2020 @ 15:00 ET

What if you could turn your organization’s vast store of documents into something useful? What if you could mine all that latent knowledge? With Azure Cognitive Search you can leverage all that content and make it instantly accessible, searchable, and actionable.

Day 5, Data Engineering, Data Preparation,
Technology

Bring Your Document Archives to Life with Azure Cognitive Search

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Keynote

Data harmonization: Support actionable insights backed by advanced analytics

Fri. September 18, 2020 @ 10:00 ET

While many leaders have called data as the ‘fuel’ for advanced analytics and AI, Shiv feels it is more than just the fuel. Fuel can be replaced, but good data is non-negotiable. A big business decision that is built using flawed data, can make an organization lose more than just millions or in some cases billions of dollars in revenue. Hence, Shiv likes to think of an accurate data set as the ‘Oxygen’ for any good statistical analysis. Join him in learning more, especially as Shiv shares how having the right data led to several winning business decisions at some of the best known Fortune 500 brands.

Day 5, Data Engineering, General Sessions, Sourcing Data,
Industry

Data harmonization: Support actionable insights backed by advanced analytics

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Live

Welcome to the Data for AI Week Conference

Mon. September 14, 2020 @ 00:00 ET

Welcome to the Data for AI Week 2020 Virtual Conference. In this session we'll go over how the event website works, highlights of the various sessions, and all the great opportunities, features, and benefits in participating in this conference.

Day 1, General Sessions,
GovernmentIndustryTechnology

Welcome to the Data for AI Week Conference

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Live

Top 10 Features in Azure Synapse for Data Engineering

Tue. September 15, 2020 @ 11:15 ET

Azure Synapse is an end to end data platform that combines data warehousing, visualization, data science, and ETL / ELT processes all in one workspace. Dayo will list out and Demo some of the Top features that makes Azure Synapse Analytics a very powerful tool to create a seamless Data Process

Day 2, Data Engineering, Data Preparation,
Technology

Top 10 Features in Azure Synapse for Data Engineering

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Keynote

Leveraging Data as a Strategic Asset

Thu. September 17, 2020 @ 16:15 ET

By increasing data-use and literacy to improve the efficiency and effectiveness of decisions, readiness, mission operations, and cybersecurity, we are changing the Air Force’s culture to be a more collaborative organization. The Air Force is facing an ever-more disruptive battlefield (i.e., information warfare, malicious cyber activity, and political information subversion). Combating these threats requires rapid advancements in our data. To ensure we have the big data necessary to support AI autonomy, we need to take existing data stovepipes and change the culture toward visible and accessible data while still allowing for security and appropriate access.

Day 4, Data Engineering, General Sessions, Sourcing Data,
Government

Leveraging Data as a Strategic Asset

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On Demand

Running cost effective big data workloads with Azure Synapse and Azure Data Lake Storage

On Demand

Learn how you can migrate expensive open source big data workloads to Azure and leverage latest compute and storage innovations within Azure Synapse and HDInsight with Azure Data Lake Storage to develop a powerful and cost effective analytics solutions.

Data Engineering, Data Preparation,
Technology

Running cost effective big data workloads with Azure Synapse and Azure Data Lake Storage

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On Demand

Power BI for BI pros and Data Engineers

On Demand

Use Power BI to build scalable analytic solutions that meet your governance needs. Also to leverage big data investments w/connections to data warehouses and apply machine learning w/o compromising performance or security.

Data Engineering, Data Preparation, General Sessions,
Technology

Power BI for BI pros and Data Engineers

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Live

Big Video Data in Retail, Defense, Healthcare & more

Wed. September 16, 2020 @ 15:00 ET

Video annotation is the new frontier of data labeling, due to rapid advances in computer vision-based machine learning and AI. However, video data is still difficult to annotate because it is highly complex, both technically and in terms of the density of annotation. Managing the annotation pipeline for video data is much harder than with images. Video is dense, hard to manipulate, and generally more difficult to make something useful out of. At Alegion, we have built a powerful, efficient, and comprehensive solution for video annotation that empowers teams to solve their largest and most complex computer vision problems.

Day 3, Data Labeling, Data Preparation,
Technology

Big Video Data in Retail, Defense, Healthcare & more

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On Demand

Building an End-to-End ML Pipeline for Big Data

On Demand

This demo-based session walks through each step in the pipeline, emphasizing best practices ranging from combining Azure Machine Learning with an Apache Spark component, such as Azure Databricks, to managing data and models across environments.

Data Engineering, Data Preparation,
Technology

Building an End-to-End ML Pipeline for Big Data

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On Demand

Urban Air Land Mobility Project with Labelbox

On Demand

Stanford masters students as part of CS230: Deep Learning describe the process and workflows for their project in which they focus on identifying suitable areas to land urban air vehicles through satellite imagery. In the recap, Andrew and Seraj share their experience completing image segmentation tasks via Labelbox’s software and labeling service, as well as lessons learned and best practices for other computer vision researchers.

Data Labeling, Data Preparation, General Sessions,
Industry

Urban Air Land Mobility Project with Labelbox

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On Demand

Labelbox’s Training data platform

On Demand

In this presentation, we’ll be presenting a walkthrough of Labelbox’s training data platform and highlighting some of the main cost and productivity benefits for data science teams.

Data Labeling, Data Preparation,
Technology

Labelbox’s Training data platform

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Live

What is a Training Data Platform and How Can It Get You to Production Faster?

Thu. September 17, 2020 @ 14:00 ET

In this presentation, Brian is going to share the key reasons for why even with all the projections that AI s going to transform businesses and industries that for leading AI teams, this is happening more slowly than most of us thought it would. Teams are hitting major roadblocks along the way and the reason is because getting high-quality labeled data and data management are at the heart of the problem.

Day 4, Data Labeling,
Technology

What is a Training Data Platform and How Can It Get You to Production Faster?

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On Demand

Leveraging Model-Assisted Labeling to Better Harness AI Data and Reduce Data Labeling Costs

On Demand

ML practitioners can dramatically reduce the time and labeling budgets by harnessing model-assisted labeling for both strong and weak supervision. Labelbox ML lead will be sharing an interactive demo and tutorial on production workflows that we have worked well with the goal of highlighting important principles that spark inspiration for others.

Data Labeling, Data Preparation,
Technology

Leveraging Model-Assisted Labeling to Better Harness AI Data and Reduce Data Labeling Costs

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On Demand

Best Practices for Imbalanced Data and Partitioning

On Demand

In this two-part learning session, we discuss best practices around data partitioning and working with imbalanced datasets. Five-fold cross-validation is often the silver bullet for partitioning your validation dataset, but there are some dangerous caveats you have to be aware of to make sure that you're building robust models. In this learning session (part 1) , we talk about those pitfalls and outline strategies for handling them. Binary target variables are very common in data science use cases, many of which are severely imbalanced. When you're building models for infrequent events, such as predicting fraud or identifying product failures, it's important to watch out for imbalance in your data. (In part 2 of this learning session we discuss strategies for working with imbalanced datasets and provide some rules-of-thumb for these types of use cases.)

Best Practices for Imbalanced Data and Partitioning

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On Demand

Data Preparation Strategies for Successful Machine Learning

On Demand

One of the most common questions about machine learning is “How do I prepare my data for a machine learning project?” In order to run successful machine learning projects, and create highly-accurate predictive models for your business, you need to prepare your data effectively. But this process doesn’t have to be a burden.

Data Preparation
Technology

Data Preparation Strategies for Successful Machine Learning

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On Demand

Enabling the AI-Driven Insurer

On Demand

For years, insurers have been using machine learning to identify fraud, develop pricing strategies, improve underwriting processes, and more. Now, a new class of automated machine learning tools is democratizing data science, making it possible for insurers of all sizes to increase ROI on machine learning initiatives and transform into AI-driven enterprises.

General Sessions
Industry

Enabling the AI-Driven Insurer

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On Demand

Deploying AI for Success on the Buy Side

On Demand

Predictive analytics is a key differentiator for asset management firms, but how does an organization do it at scale for business impact? Buyside firms — and the broader financial services industry — are capitalizing on AI advances to predict activities across the front, middle and back office to increase revenue, improve efficiencies, reduce costs — and improve risk management.

General Sessions
Industry

Deploying AI for Success on the Buy Side

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On Demand

Using Small Datasets to Build Models

On Demand

As the global coronavirus pandemic is causing major disruptions to communities and the economy, many existing data science models struggle to adapt to these shifts due to a shortage of available data. learn more about: Strategies to build a "cold start" model. Checks to ensure you have meaningful, consistent signal from limited examples. Diving deeper into model insights to verify meaningful model fit.

Data Engineering, Data Preparation, Sourcing Data,
Technology

Using Small Datasets to Build Models

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On Demand

How Retailers are Embedding AI

On Demand

The Empowered Consumer is more connected and informed than at any other time in retail history. Retailers must now start anticipating and predicting their consumer’s evolving needs and habits by transforming their business decisions through embedding AI in their data-driven culture. Learn how best-in-class retailers are using AI and machine learning to identify signals and patterns in their data to become truly customer-centric.

General Sessions
Industry

How Retailers are Embedding AI

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On Demand

The Empowered Consumer: Adapting to Changing Buyer Demands with AI

On Demand

Companies across industries face new challenges connecting with empowered consumers. Customers have been completely altering their shopping habits in recent months like never before. With shops closed or limited entry, purchasing has shifted to online, loyalty has shifted by industry and brand, safety is paramount, and all of it varies by geography. Learn how best-in-class retailers are using AI and machine learning to identify signals in their data to become truly customer-centric.

General Sessions
Industry

The Empowered Consumer: Adapting to Changing Buyer Demands with AI

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Live

Preparing your data for machine learning workloads

Mon. September 14, 2020 @ 16:15 ET

Machine learning outcomes are only as good as the data they are built on, but preparing data for these advance workloads can be time-consuming and difficult to scale, especially if you are looking to implement machine-learning applications that rely on data from across your entire organization. In this session, Ben Snively will share some best practices related to collecting, storing, and processing big data and disparate data sets so that you glean intelligent insights from your machine-learning algorithms. We will share some architectural design patterns.

Day 1, Data Preparation,
Technology

Preparing your data for machine learning workloads

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On Demand

Visualizing and Communicating the Water Pipe Replacement Program in Flint, Michigan

On Demand

In this session, we’ll discuss how a predictive algorithm helped the City of Flint focus their service line (water pipe) investigations in the areas at highest risk for having lead or galvanized steel service lines. We’ll discuss our work (in progress) to create a public map using best visual data and public health communication practices that, when completed, will allow Flint residents to visualize the predictive model outcomes and the pipe replacement progress in the city.

General Sessions
Industry

Visualizing and Communicating the Water Pipe Replacement Program in Flint, Michigan

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On Demand

Building Your Next Machine Learning Data Set

On Demand

Gathering an initial data set for your machine learning project is the first hurdle on the path to a successful machine learning algorithm. How do you get your hands on the perfect data set? We joined our partners at Keymakr to discuss the attributes of an ideal data set, the pros and cons of using a pre-created data set, and some best practices for building your own.

Building Your Next Machine Learning Data Set

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Live

Meeting the Challenge of Data Privacy & Ethics for AI (Panel)

Fri. September 18, 2020 @ 09:00 ET

When using customer data for machine learning models, there are some considerations around data privacy and ethics of data collection, storage, and usage of that data. Companies have long treated data as assets, so it should come as no surprise that they take the use of this data seriously. Organizations are increasingly establishing rigorous governance processes around their data management. This panel will explore how various industries and companies are approaching the challenge of data privacy & ethics for AI.

Day 5, General Sessions,
Industry

Meeting the Challenge of Data Privacy & Ethics for AI (Panel)

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Live

Sharing AI Rather than Data

Wed. September 16, 2020 @ 16:15 ET

The COVID-19 pandemic has dramatically expanded telehealth services and, with it, highlighted the need to share analytics across networks without moving data.The promise of applying Artificial Intelligence (AI) to all of this data for more personalized and wholistic diagnosis and treatment is threatened by communications challenges and eroding patient trust. In this presentation, we will explore how sharing analytics rather than data solves these problems and more.

Day 3, General Sessions, Sourcing Data,
Government

Sharing AI Rather than Data

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On Demand

How to build world-class AI applications with Labelbox

On Demand

During the session, we'll be covering: - Some best practices for AI in Production - Produce demo and overview - How a training data platform helps you optimize data labeling costs - How Labelbox helps AI-focused product leaders get to production faster - The specific features that will help you decrease costs, improve automation and collaboration for your ML products and projects.

Data Labeling, Data Preparation,
Technology

How to build world-class AI applications with Labelbox

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Live

How data and machine learning are driving innovation across agencies

Fri. September 18, 2020 @ 16:15 ET

Learn how data and machine learning are driving innovation across agencies.

Day 5, Data Engineering, General Sessions,
Government

How data and machine learning are driving innovation across agencies

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Live

The VC Landscape for AI companies (Panel)

Fri. September 18, 2020 @ 15:00 ET

It’s no secret that companies with an AI focus have been receiving lots of funding these days. It’s always interesting to get venture capitalists (VCs) perspectives on the market and how new technologies are trending. In this panel we will discuss what thecurrent climate is for AI companies looking for funding, and what makes companies stand out to investors

Day 5, General Sessions,
Industry

The VC Landscape for AI companies (Panel)

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Live

Building User-Friendly Data tools for Technical and Non-technical users

Mon. September 14, 2020 @ 14:00 ET

This talk will focus on how teams can adopt an open source approach in developing data and machine learning learning based solutions. We’ll explore how we can create a reusable code-base and leverage it to build powerful data applications. We will explore scenarios on how an analyst vs an executive can benefit from the same codebase. If this sounds fun, tag along.

Day 1, Data Engineering, Data Preparation, General Sessions,
Technology

Building User-Friendly Data tools for Technical and Non-technical users

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Live

GSA CoE Experience With Data Wrangling for AI

Wed. September 16, 2020 @ 11:15 ET

Given the GSA Centers of Excellence's government wide perspective, Alex and Krista will share their experience from on the ground data management and implementation to help agencies prepare for Artificial Intelligence as an enabling technology to help support mission delivery. They will share best practices and lessons learned from across government.

Day 3, Data Engineering, Data Preparation, General Sessions,
Government

GSA CoE Experience With Data Wrangling for AI

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On Demand

5 QA Methods to Win the Race to Quality Data

On Demand

The end result for every data labeling project is quality data - but how do you get there? There are several quality assurance workflow types but each has pros and cons when it comes to the quality and speed of data outputs. When you're evaluating data labeling providers or planning in-house processes, you should consider which QA workflow will work best for your business and data needs.

5 QA Methods to Win the Race to Quality Data

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On Demand

Data Prep: What Data Scientists Wish You Knew

On Demand

If you’re considering a machine learning project, you probably know that you need data, and lots of it. And while many companies are swimming in volumes of data, that data is almost never ready for AI and ML projects. It must be prepared, which can include cleansing, annotation, and more.

Data Engineering, Data Preparation,
Technology

Data Prep: What Data Scientists Wish You Knew

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On Demand

Conversational AI: Making it Smarter & Scalable

On Demand

Learn how conversational AI systems are being made more intelligent and scalable.

Data Labeling, Data Preparation,
Technology

Conversational AI: Making it Smarter & Scalable

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On Demand

Comprehensive Data Pipelines for Automotive AI

On Demand

It’s no secret that there is an enormous business opportunity with the rise of autonomous vehicles and the connected car. Whether you are building a fully autonomous vehicle, improving driver assistance features, or in-cabin experience, high-quality annotated training data is the key to effective AI systems. This session will help take you from Level 1 to Level 5 autonomy, driving you ahead of the competition.

Data Engineering, Data Labeling, Data Preparation,
Industry

Comprehensive Data Pipelines for Automotive AI

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On Demand

Building Ethics and Diversity in AI

On Demand

Bias in machine learning is a significant concern as technology gets increasingly ubiquitous across many industries. Some types of bias can be attributed to limits in design and tooling; however, the bias in the training data itself is a general phenomenon. Skewed training data propagates into discriminatory AI models that amplify human prejudices. Building a data labeling framework that uses a diverse set of crowd workers to collect and label the data can help reduce bias.

Data Engineering, General Sessions,
Industry

Building Ethics and Diversity in AI

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On Demand

From Prototype to Production – Building Successful Computer Vision Models at Scale

On Demand

Are you training a self-driving car, detecting animals with drones, monitoring equipment for predictive maintenance, or identifying car damage for insurance claims? The challenges to effectively train, deploy & tune a computer vision model at scale remain the same.

Data Engineering, Data Labeling, Data Preparation,
IndustryTechnology

From Prototype to Production – Building Successful Computer Vision Models at Scale

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On Demand

Training Conversational Agents on Noisy Data

On Demand

This session will address both sides of the challenge: (1) using data-efficient strategies during the utterance collection and annotation phases to optimize the trade-off between cost and quality when collecting training data, (2) using data-driven approaches to train and generate behaviors for a conversational agent despite noisy data and lack of training labels.

Data Labeling
Technology

Training Conversational Agents on Noisy Data

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Live

Automating Data Collection at Morningstar

Fri. September 18, 2020 @ 15:00 ET

At Morningstar, we’re leveraging Machine Learning to collect financial data on many different instrument types across global markets. A task that had been executed manually in the past is now becoming highly optimized to deliver scale and quality by leveraging Machine Learning. We’re building a self-sustaining model improvement lifecycle that includes automated continuous feedback collection, retraining and deployment.

Day 5, Data Engineering, Data Preparation,
Industry

Automating Data Collection at Morningstar

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Live

The View from Washington

Thu. September 17, 2020 @ 11:15 ET

AI has fast emerged as a top issue for policymakers at the highest levels of government in Washington, DC. Jeremy Bash will discuss macro trends affecting AI in the U.S.and why policymakers must strengthen domestic AI capabilities, ensure that trust and bias prevention are enshrined in AI platforms, and inspire the next generation of AI engineers.

Day 4, General Sessions,
Government

The View from Washington

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Live

Enabling AI Innovation via Data and Model Sharing

Wed. September 16, 2020 @ 09:00 ET

This talk will provide an overview of the NSF Convergence Accelerator program (C-Accel) and Track D of the Accelerator on Enabling AI Innovation via Data and Model Sharing.

Day 3, Data Engineering, General Sessions,
Government

Enabling AI Innovation via Data and Model Sharing

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Keynote

Keynote: The Past, Present and Future of DOE Leadership in AI

Wed. September 16, 2020 @ 10:00 ET

Cheryl Ingstad, Director of the Artificial Intelligence & Technology Office (AITO) for the Department of Energy (DOE) will share how DOE (including its 17 national labs) is leading the federal government in the development and application of AIto strengthen its core missions of energy, cyber, and national security and accelerate scientific discovery. Join us for this interactive discussion followed by Q&A.

Day 3, General Sessions,
Government

Keynote: The Past, Present and Future of DOE Leadership in AI

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Live

Building Performant GEOINT Dashboards with OmniSci Immerse & Jupyter Notebooks

Tue. September 15, 2020 @ 14:00 ET

This workshop will present some typical GEOINT applications delivered as interactive dashboards and will drill down into the construction of one of them in detail. Attendees will learn all three levels of a modern web stack.

Day 2, Data Engineering, Demo Showcase, General Sessions, On-Demand AI Experiences,
Government

Building Performant GEOINT Dashboards with OmniSci Immerse & Jupyter Notebooks

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Live

Utilizing Robotic Process Automation and Artificial Intelligence on Publicly Available Procurement Data

Tue. September 15, 2020 @ 16:15 ET

GSA released a publicly available Request For Information to the industry in order to explore the possibilities of artificial intelligence in data analysis of publicly available procurement data. We requested an analysis and aggregation of publicly-available government-wide data sources of their choosing.

Day 2, General Sessions,
Government

Utilizing Robotic Process Automation and Artificial Intelligence on Publicly Available Procurement Data

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Live

The Ethical Side of Data Usage – Industry Perspective (Panel)

Mon. September 14, 2020 @ 15:00 ET

Machine learning requires data, and organizations of all types and in different industries have lots of data that is useful for many very important tasks. However enterprises are finding that there are concerns and restrictions on how that data can be used, shared and applied. This panel will explore the ethical side of data usage from an industry perspective.

Day 1, General Sessions,
Industry

The Ethical Side of Data Usage – Industry Perspective (Panel)

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Live

The Future of Human-powered Data Annotation

Wed. September 16, 2020 @ 09:00 ET

Innovation in AI is accelerating at a steep rate, and the tools and processes used to support such AI innovation are no exception. Long the domain of a human workforce, data annotation is seeing advances, such as using AI and automation to pre-label data and conduct quality assurance, that seemingly promise to eliminate the need for human-powered labeling. This panel will explore the future of humans-in-the-loop data annotation and what role your labeling workforce will play in the years to come.

Day 3, Data Labeling, Data Preparation,
Technology

The Future of Human-powered Data Annotation

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On Demand

Humans in the Loop: From Proof of Concept to Production

On Demand

The role of humans isn’t limited to training data preparation -- it extends across the entire model development process. Join CloudFactory for a look at how you can deploy humans in the loop throughout the AI lifecycle -- from proof of concept to production -- to improve model output, reduce costs, and accelerate model development.

Data Engineering, Data Labeling, Data Preparation,
Technology

Humans in the Loop: From Proof of Concept to Production

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Live

Machine Learning from the Trenches: the Artificial Intelligence, Analytics and Innovation Division at the Jet Propulsion Laboratory

Mon. September 14, 2020 @ 11:15 ET

Chris Mattmann will explain how JPL’s Division 174 for AI, Analytics, and Innovation in the Information Technology and Solutions Directorate (ITSD) supports advanced analytics, AI, and Machine Learning for Smarter Rovers, a Smarter Campus, and beyond!

Day 1, Data Engineering, General Sessions,
Government

Machine Learning from the Trenches: the Artificial Intelligence, Analytics and Innovation Division at the Jet Propulsion Laboratory

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Live

Using Predictive AI to Optimize the Grid

Wed. September 16, 2020 @ 16:15 ET

In this session you will learn about how Veritone uses AI to route energy like data, stabilize the grid, and make green energy the efficient, low-cost source it was meant to be. You will understand the key components to Veritone’s patented energy automation solution: demand forecaster, energy optimizer, device controller, and energy arbitrage.

Day 3, General Sessions,
Industry

Using Predictive AI to Optimize the Grid

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Live

MLOps Done Right: Best Practices to Deploy, Integrate, Scale, Monitor, and Comply

Tue. September 15, 2020 @ 16:15 ET

Successful MLOps not only requires strong collaboration between the AI data team, AI model team, and DevOps- it’s the ability to effectively manage and mitigate risk across the deployment, integration, scale, monitoring, and compliance stages of an AI project. Learn more about ML Ops and implementations in this session.

Day 2, Data Engineering, General Sessions,
Technology

MLOps Done Right: Best Practices to Deploy, Integrate, Scale, Monitor, and Comply

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Live

How AI Transforms Contact Centers in the Age of COVID

Thu. September 17, 2020 @ 14:00 ET

Organization contact centers face new challenges during the pandemic as they grapple with spikes in demand, a remote workforce, and compliance concerns. These challenges create opportunities for AI automation solutions that deploy digital workers to come alongside human workers and make their jobs easier.

Day 4, General Sessions,
Industry

How AI Transforms Contact Centers in the Age of COVID

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Live

Building Trust in Your AI

Mon. September 14, 2020 @ 15:00 ET

Many AI projects fall short of expectations due to poor model performance or the unintended consequences of inaccurate AI decisions. What if there was a universal way for MLOps/AIOps to evaluate and monitor the performance and behavior of AI models, both pre-deployment and ongoing, no matter the vendor or features used? In this session, we will review the pitfalls of opaque AI models, and discover how to evaluate, compare, and monitor performance and behavior across AI models, for better AI model trust and explainability.

Day 1, Data Engineering, General Sessions,
IndustryTechnology

Building Trust in Your AI

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On Demand

Performant, Cost-Effective 5G Planning at Scale

On Demand

In this session, experience GPU-accelerated analytics through a real-life 5G network planning demo. Learn how OmniSci can be used to combine RF mapping and demographic data sets at scale using service quality and NPV measures to optimize both cost-effectiveness and performance.

Data Engineering, General Sessions,
Industry

Performant, Cost-Effective 5G Planning at Scale

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Live

The Importance of Analysis-Ready Data to Powerline Fire Safety Planning using GPU-Accelerated Analytics

Fri. September 18, 2020 @ 14:00 ET

Conventional vegetation management by power utilities has been based on 3-year revisit scheduling and largely ground-based monitoring. These efforts have not been minor-utilities spend rather many millions of dollars per year. But recent events in California have made it clear that they are completely insufficient. At OmniSci, we have developed an alternative solution based on continuous monitoring, data, and fire science. Our system integrates near real-time satellite, LIDAR, and weather and micro-demographics data in order to assess risk dynamically.

Day 5, General Sessions,
Industry

The Importance of Analysis-Ready Data to Powerline Fire Safety Planning using GPU-Accelerated Analytics

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Live

COVID-19 and the Data Lake: How Maverick AI can Assist Monitoring and Tracing to Predict the Next Outbreak

Thu. September 17, 2020 @ 09:00 ET

AI has the capability to leverage large amounts of data to make predictions and do analysis. This capability will be especially useful to address the COVID-19 pandemic, with the ability to conduct early disease monitoring to predict future outbreaks and control further spread. In this session, hear from panelists and learn about how MTX Group, Inc. is leveraging Maverick AI and the Data Lake concept to help states control the pandemic.

Day 4, Data Engineering, Data Preparation,
Industry

COVID-19 and the Data Lake: How Maverick AI can Assist Monitoring and Tracing to Predict the Next Outbreak

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Live

Accelerating AI with Machine Learning Operations

Fri. September 18, 2020 @ 16:15 ET

Presenting an overview of DataRobot's MLOps to monitor and manage your machine learning models - what works, what doesn't, and what to do about it.

Day 5, Data Engineering,
Technology

Accelerating AI with Machine Learning Operations

View Session
Live

What Did We Learn Labeling 13 Billion Units of Training Data?

Wed. September 16, 2020 @ 14:00 ET

The AI system you build is as good or bad as the data you have trained it on. Whether you are training a self-driving car, building a customer service chatbot, or diagnosing diseases with AI, a scalable training data strategy is integral to your success. In this session, we will discuss how much training data is enough, how to effectively manage quality, quantity, and throughput in your data, and more!

Day 3, Data Labeling,
IndustryTechnology

What Did We Learn Labeling 13 Billion Units of Training Data?

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Live

The Costs of Not Having an AI Strategy and How to Prepare for AI (Keynote)

Thu. September 17, 2020 @ 10:00 ET

AI is here and is already having a massive impact on public and private sector organizations. There is a huge cost financially, operationally, and competitively for not being prepared. In this session, MTX Group, Inc. will discuss the Maverick AI platform and how we work with public and private sector organizations to understand, prepare for, and implement AI solutions

Day 4, Data Engineering, General Sessions,
Industry

The Costs of Not Having an AI Strategy and How to Prepare for AI (Keynote)

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Live

Efficiency and Effectiveness – How Maverick AI Has Accelerated the Private and Public Sectors

Mon. September 14, 2020 @ 09:00 ET

It is essential for both public entities and private businesses to prioritize efficiency and effectiveness in their work. This has become even more important in the COVID-19 era, where organizations are faced with stretched budgets and a changing workforce landscape. In this session, learn how the Maverick AI platform has been essential in achieving success for both public and private sector clients, and what the public and private sectors are thinking about as they consider implementing AI.

Day 1, Data Engineering, General Sessions,
GovernmentIndustry

Efficiency and Effectiveness – How Maverick AI Has Accelerated the Private and Public Sectors

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Live

AI for Everyone: How LinkedIn Builds Holistic AI at Scale

Thu. September 17, 2020 @ 16:15 ET

LinkedIn serves close to 700 million members in more than 200 countries, and AI is woven into virtually every experience on the site. Last year alone, members viewed nearly 400 billion feed updates, and the rate of content creation on the site is rapidly expanding. To maximize a useful, personalized experience, LinkedIn uses AI to customize things like a user's feed, job notifications, and learning content.

Day 4, Expert Sessions, General Sessions, On-Demand AI Experiences,
Industry

AI for Everyone: How LinkedIn Builds Holistic AI at Scale

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Live

Classifying Text With Neural Networks

Wed. September 16, 2020 @ 16:00 ET

Six years ago BLS staff read and manually classified hundreds of thousands of written descriptions of work-related injuries and illnesses each year. Today, more than 85% of these classifications are assigned by a deep neural network that is more accurate, on average, than trained human workers. In this presentation, Alex will discuss how BLS addressed some of the many challenges inherent in this transition including how to build these systems, how to decide when and how to use them, and how to monitor and maintain them to continually improve performance.

Day 3, Data Engineering, General Sessions,
Government

Classifying Text With Neural Networks

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Live

Augmented Intelligence: The Power of a Human-in-the-Loop

Thu. September 17, 2020 @ 11:15 ET

Since his co-authored Harvard Business Review article and Only Humans Need Apply book in 2015, Tom Davenport has argued that augmentation is a more likely and more desirable outcome for organizations and people than large-scale automation. Over the last year he’s been gathering detailed examples of human workers who toil alongside smart machines. In this presentation he’ll describe what “the future of work now” looks like and how people, organizations, and machines will need to change to facilitate this outcome.

Day 4, Data Engineering, Data Labeling, Data Preparation,
Industry

Augmented Intelligence: The Power of a Human-in-the-Loop

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Keynote

Data for AI – Today and investing in the future – Suzette Kent [Keynote]

Tue. September 15, 2020 @ 10:00 ET

This session will highlight actions and initiatives to accelerate the use of data inside the Federal government that are currently happening as well as outline investments and actions needed to evolve operating culture and build the workforce for the future. Hear about key elements and Agency specific activities ranging from Federal Data Strategy and Agency AI efforts. Kent will also share thoughts on actions needed to continue to develop workforce for today and the emerging jobs of tomorrow. Actions that include educational shifts, cultural shifts and commitments from leaders inside government and across private sector.

Day 2, General Sessions, Sourcing Data,
Government

Data for AI – Today and investing in the future – Suzette Kent [Keynote]

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Live

How to Cognitively Enrich Data in Legacy Systems

Wed. September 16, 2020 @ 11:15 ET

Valuable unstructured content is everywhere in organizations. Video, audio, and text content abounds, just waiting to be discovered and utilized--whether within BPA and RPA processes or stored in content management and line of business systems. The challenge is, how to enrich and extract that content for better findability and insight, and how to do it quickly, easily and at low cost? Join us in this session as we explore how to leverage an expansive ecosystem of hundreds of ready-to-deploy AI models to extract business value from content, and how to do so at scale, in near real time, without the need for AI expertise.

Day 3
IndustryTechnology

How to Cognitively Enrich Data in Legacy Systems

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Live

How AI is Helping Public Safety Agencies Demonstrate Greater Transparency

Wed. September 16, 2020 @ 14:00 ET

Amidst challenging times for law enforcement, police departments and other public safety agencies have an opportunity to foster greater public trust and increased transparency through the application of AI. In this session, Retired Chief David Jantas from the Pemberton Township, NJ Police Department will share his experiences and best practices on how agencies are leveraging AI driven technology to not only help save costs and resources, but provide greater transparency to the public such as the release of redacted public records requests.

Day 3, General Sessions,
Government

How AI is Helping Public Safety Agencies Demonstrate Greater Transparency

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Live

Intelligent Data Understanding for AI

Tue. September 15, 2020 @ 09:00 ET

This presentation will discuss benefits and applications of “smart data” and intelligent data understanding for operationalizing AI. Techniques that enable and benefit from smart data are data discovery, machine learning, knowledge graphs, semantic linked data, knowledge discovery, and knowledge management. Intelligent data understanding thus meets the needs for AI operations, which must devour streams of data – not just any data, but smart data – the right data at the right time in the right context.

Day 2, Data Engineering, Data Preparation, General Sessions,
Technology

Intelligent Data Understanding for AI

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Keynote

Dealing with the Cultural Change involved with Data and AI (Keynote)

Thu. September 17, 2020 @ 13:00 ET

In this keynote Jose Arrieta, Former CIO and acting CDO at the United States Department of Health & Human Services (HHS) will share his experiences dealing with the cultural change involved with AI, data, and transformative technology. He will share how as CIO and interim CDO he dealt with key issues around data security, data privacy, data transparency, and sharing data with integrity. He will share how AI and data force organizations and agencies to focus on those topics, and why it’s so important to have meaningful discussions around these topics.

Day 4, General Sessions,
Government

Dealing with the Cultural Change involved with Data and AI (Keynote)

View Session
Keynote

Robust Machine Learning for Usable Decision Systems

Wed. September 16, 2020 @ 13:00 ET

Practical decision systems require much more than end-to-end learned models. This talk will focus on research and engineering questions on machine learning robustness and the two broad categories of work in that area.

Day 3, Data Engineering, Data Preparation, General Sessions, Sourcing Data,
Industry

Robust Machine Learning for Usable Decision Systems

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Live

Challenges in Data Management in the Government (Panel)

Tue. September 15, 2020 @ 14:00 ET

For many in government, they know that the government is drowning in data. However, unlike private industry there are some unique challenges that Governments have when it comes to data collection and data management. In this panel we’ll have folks from various government agencies including both civilian and DoD to share challenges in data management and what agencies can do to overcome these challenges

Day 2, Data Engineering, General Sessions,
Government

Challenges in Data Management in the Government (Panel)

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Keynote

Data, AI, and Hyperdisruption [Keynote: Anthony Scriffignano, Dun & Bradstreet]

Fri. September 18, 2020 @ 13:00 ET

Today, more than ever, decisions are informed by data, and increasingly by digital technology that uses data in new and sometimes unexplainable ways. Everything produces data. Even data begets data. Dr. Anthony Scriffignano, SVP/Chief Data Scientist at Dun and Bradstreet, will discuss some of the ways in which hyperdisruption is challenging business leaders to think differently about making decisions with data. He will share best practices, discuss challenges and future risks, and explore models for problem and opportunity formulation that are helpful in this kinetic environment.

Day 5, Data Engineering, Data Preparation, General Sessions, Sourcing Data,
Industry

Data, AI, and Hyperdisruption [Keynote: Anthony Scriffignano, Dun & Bradstreet]

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Keynote

KEYNOTE: Do we really need more data?

Tue. September 15, 2020 @ 13:00 ET

Once upon a time working on artificial intelligence or machine learning meant constantly yearning for data and struggling to find cool problems to work on for which there was some. Now data’s aplenty but that old longing for data seems to have left a deep scar. Will more data by itself make the machine intelligent? What if making AI business-ready for you was altogether different than collecting data? What if it had to do with the (business) questions you ask? That sounds like a conversation worth starting. Introduced by: Kathleen Walch, Managing Partner & Principal Analyst, Cognilytica

Day 2, Data Engineering, General Sessions,
Industry

KEYNOTE: Do we really need more data?

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Live

Solving Challenging E-commerce Problems Using the Power of Data Science

Mon. September 14, 2020 @ 16:15 ET

The Retail industry has been disrupted by the e-commerce revolution more than any other industry. As a Director of Core Data Science at The Home Depot which is the largest home improvement retailer in the world, I deal with the challenges of building such systems utilizing the cutting-edge technologies in AI, machine learning, and data science. In this talk, I would like to discuss and highlight how we have leveraged different aspects of AI to solve challenging e-commerce problems for HomeDepot

Day 1, Data Engineering, Data Preparation, Expert Sessions, General Sessions,
Industry

Solving Challenging E-commerce Problems Using the Power of Data Science

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Live

The Ethical Side of Data Usage – Government Perspective (Panel)

Mon. September 14, 2020 @ 14:00 ET

Machine learning requires data, and many companies have lots of data that is useful for many very important tasks. However there are many questions about how this data should be used, shared and applied. Additionally, companies walk a fine line with just how much they want to let customers and users know about the data they have on them. This panel will explore the ethical side of data usage from an industry perspective.

Day 1, Expert Sessions, General Sessions,
Government

The Ethical Side of Data Usage – Government Perspective (Panel)

View Session
Live

Data considerations in Banking and Finance for AI (Panel)

Mon. September 14, 2020 @ 11:15 ET

For many years banks and financial institutions have been at the forefront of using technology to help with many operations and processes. In this panel we’ll explore how various financial companies are using data and machine learning to help catch fraud, improve banking processes, and improve the overall customer experience.

Day 1, Data Engineering, Data Preparation, General Sessions,
Industry

Data considerations in Banking and Finance for AI (Panel)

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Live

Data at the State and Local Level

Tue. September 15, 2020 @ 11:15 ET

This panel will address key challenges for data and AI at the state and local government level

Day 2, General Sessions,
Government

Data at the State and Local Level

View Session
Live

Predictive Analytics and Data

Fri. September 18, 2020 @ 11:15 ET

One of the seven patterns of AI, predictive analytics is being used in many areas of government to help humans make better decisions. Some examples of this pattern being applied include assisted search and retrieval, predicting some future value for data, predicting behavior, predicting failure, giving advice, and intelligent navigation. The idea is that it helps to make better decisions, providing augmented intelligence capabilities. Machine learning is what is helping to make the decision, adapting over time to provide better results.In this panel we’ll explore how various agencies are successfully applying the predictive analytics pattern of AI.

Day 5, Data Engineering, Data Preparation, General Sessions,
Government

Predictive Analytics and Data

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Keynote

AI Projects at the National Science Foundation (NSF)

Mon. September 14, 2020 @ 10:00 ET

The National Science Foundation supports research on questions at the heart of many of our national priorities. The broad behavioral, social and economic impacts of artificial intelligence constitute one such priority. In this interactive keynote Dorothy Aronson will share how the NSF plans to embrace AI technologies, what AI projects they are currently funding, and how AI will impact the agency in the coming years.

Day 1, Data Engineering, Data Preparation, General Sessions,
Government

AI Projects at the National Science Foundation (NSF)

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On Demand

Data Prep for AI and ML

On Demand

The AI revolution is here, but it has demands. AI needs clean, contextual data in the proper format to do its job. Too often, though, that process of cleaning and contextualizing takes an extraordinary amount of time—time that could be spent refining your predictive models.

Data Preparation
Technology

Data Prep for AI and ML

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On Demand

Massively Accelerated Analytics & Data Science

On Demand

Massively Accelerated Analytics & Data Science

Data Engineering, General Sessions,
Technology

Massively Accelerated Analytics & Data Science

View Session
On Demand

Predict and Change the Future of your Network and Multiple Sources

On Demand

Telco markets worldwide are highly competitive. Consumers have more options than ever to satisfy their telecommunications needs. This means providers are competing harder, and paying closer attention to critical factors like cost of customer acquisition, average revenue per unit (ARPU), and customer churn. The smartest providers understand the tight relationship between these issues and their massive, and growing, stockpiles of data. Fortunately, there are new technologies that can handle billions of rows of data, joined across multiple datasets, with millisecond filtering and visualization times. With OmniSci, massive customer data is an asset instead of a barrier.

Data Engineering, General Sessions,
Industry

Predict and Change the Future of your Network and Multiple Sources

View Session
On Demand

AI Driven Business Assurance for 5G

On Demand

How to use AI to assure 5G services, including 5G slicing, perform as planned with API-based use cases including customer experience, churn-management and fraud-management.

Data Engineering, General Sessions, On-Demand AI Experiences,
Industry

AI Driven Business Assurance for 5G

View Session
On Demand

Finding Purposefully Hidden Sites with GPUs and ML

On Demand

Abstract: Finding purposely-hidden nuclear sites is hard. But new tools and datasets allow analysts to interactively explore huge geotemporal datasets. OmniSci has recently partnered with the Center for Nonproliferation Studies (CNS) and Planet to demonstrate how daily satellite imagery, machine learning for feature extraction, and interactive analytics can help make the world safer. CNS continually assesses potential nuclear missile production sites. It has found that in North Korea these are often hidden at the ends of new mountain roads. How can we turn this insight into actionable data?

Data Engineering, General Sessions,
Government

Finding Purposefully Hidden Sites with GPUs and ML

View Session
On Demand

Applying Data to the COVID-19 Pandemic with OmniSci

On Demand

Abstract: Data and analytics have played a central role in the first wave of the global response to COVID-19. From the earliest days of the pandemic, we’ve had up-to-date reports of case counts, fatalities and recoveries gathered by industrious volunteers everywhere. See how OmniSci and our partners at AWS, Safegraph, Veraset, and X-Mode are using anonymized, data-driven methods to contribute to relief efforts at a national scale for the next phase of the COVID-19 response efforts.

Data Engineering, Data Preparation, General Sessions, On-Demand AI Experiences,
Industry

Applying Data to the COVID-19 Pandemic with OmniSci

View Session
On Demand

Explore Global Confirmed COVID-19 Cases & Spread with OmniSci

On Demand

In this demo video we explore the COVID-19 pandemic, filtering across location and time. OmniSci's demo allows users to visualize the spread of the virus using maps and charts, compare the growth of cases across various countries and US states, and analyze the recovery rate in various regions of the world. This demo includes data from Johns Hopkins (https://coronavirus.jhu.edu/) and is updated daily.

Data Engineering, Data Preparation, General Sessions,
GovernmentIndustry

Explore Global Confirmed COVID-19 Cases & Spread with OmniSci

View Session
On Demand

COVID-19 Shelter in Place with X-Mode

On Demand

Watch as we look at areas that are adhering to shelter in place policies and to what degree there is a correlation between adherence and the spread of the disease. With this movement data, we can determine home location then visualize if devices are at home, for how long, track changes over time and correlate to the spread of the disease. X-Mode’s dataset used in this research is aggregated and generalized. X-Mode does not collect or share any personally identifiable information such as name, email, or phone number. All devices have given consent to location collection.

Data Engineering, Data Preparation, General Sessions,
Industry

COVID-19 Shelter in Place with X-Mode

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On Demand

COVID-19 Movement Visualization with X-Mode

On Demand

Check out how OmniSci can explore movement patterns and population movement easily on billions of records. Here, you will see how we can look through space and time to observe Italian travelers as they come to NYC and other locations around the world. X-Mode’s dataset used in this research is aggregated and generalized. X-Mode does not collect or share any personally identifiable information such as name, email, or phone number. All devices have given consent to location collection.

Data Engineering, Data Preparation, General Sessions,
Industry

COVID-19 Movement Visualization with X-Mode

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On Demand

COVID-19 County Level Infection Data with Jupyter Labs

On Demand

See how we leverage data provided by the New York Times including population demographics, movement patterns and external factors such as weather. OmniSci can look at county level infections to understand the current situation and use Jupyter Labs to test hypotheses and predict future events.

Data Engineering, Data Preparation,
Industry

COVID-19 County Level Infection Data with Jupyter Labs

View Session
On Demand

Tracking Public Adherence to Social Distancing Policies

On Demand

In this video we show how OmniSci can be used to track the effectiveness of social distancing policies to help fight the spread of COVID-19. Using premium location data from our partner, X-Mode, we aggregate 4.5 billion anonymized location records at the county level. We can slide across this data over the time, revealing how well counties are sheltering in place.

Data Preparation, General Sessions,
Industry

Tracking Public Adherence to Social Distancing Policies

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On Demand

Massive Scale Contact Tracing of COVID-19 Hotspots

On Demand

For this dashboard we dug into an April 25th, 2020 Washington Post article about COVID-19 hotspots at US meat processing plants. Using data from our partners at X-Mode and Safegraph, we analyze activity at these plants as well as the COVID-19 infections in nearby communities. Finally, we trace activity from one plant to identify potential virus hotspots in nearby locations. X-Mode’s dataset used in this research is aggregated and generalized. X-Mode does not collect or share any personally identifiable information such as name, email, or phone number. All devices have given consent to location collection.

General Sessions
GovernmentIndustry

Massive Scale Contact Tracing of COVID-19 Hotspots

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On Demand

When Data Science Becomes an Art: Forecasting when Historical Data Is Irrelevant

On Demand

2020’s word of the year is likely to be “unprecedented” — which might make us rather nervous as data scientists or analytics professionals. Machine learning is, after all, the art and science of predicting outcomes of upcoming events based on historical data; in a period without precedent, most available data describes a different, bygone world. In this session, we discuss how this affects our ability to build useful predictive analytics models for the current environment and how to ensure that data science and machine learning continue to add business value.

General Sessions
Technology

When Data Science Becomes an Art: Forecasting when Historical Data Is Irrelevant

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On Demand

Visual AI

On Demand

How can you incorporate images into your model workflows? DataRobot can easily build and deploy highly accurate and explainable machine learning models with images using commodity hardware.

Data Engineering, General Sessions,
Technology

Visual AI

View Session
On Demand

Automated Time Series

On Demand

This session shows how to use DataRobot’s Automated Time Series. DataRobot builds for the preprocessing and construction of sophisticated time series models that predict the future values of a data series based on its history and trend.

Automated Time Series

View Session
On Demand

Data Prep 101

On Demand

Walkthrough on key data prep functions, such as cluster, edit, and dataset linking.

Data Preparation
Technology

Data Prep 101

View Session
On Demand

Trusted AI: Values, Understanding, and Reliability

On Demand

Learn the importance of understanding your models and how that can help counter biases to help build more reliable models.

Trusted AI: Values, Understanding, and Reliability

View Session
On Demand

Deploy and Monitor Models with DataRobot MLOps

On Demand

The DataRobot platform allows for deployment using native DataRobot models, custom-trained models using containers, and monitoring prediction servers using the DataRobot agent. We cover each of these modalities and provide attendees with example code.

Data Engineering, General Sessions,
Technology

Deploy and Monitor Models with DataRobot MLOps

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On Demand

Deep Learning for Tabular Data: A Bag of Tricks

On Demand

Jason McGhee, Senior Machine Learning Engineer at DataRobot, has been spending time applying deep learning and neural networks to tabular data. Although the deep learning technique can prove challenging, his research supports how valuable it is when using tabular datasets. In this video, Jason shares some important techniques for implementing deep learning when learning heterogeneous tabular data.

Data Engineering, Data Preparation,
Technology

Deep Learning for Tabular Data: A Bag of Tricks

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On Demand

Start Modeling with AutoML

On Demand

This session is an introduction to the DataRobot platform. It will focus on how to use DataRobot to quickly build, interpret, and implement highly accurate machine learning models.

Data Engineering, General Sessions,
Technology

Start Modeling with AutoML

View Session
On Demand

Powering Applications and Services with DataRobot (Practical Introduction to APIs)

On Demand

DataRobot offers a comprehensive set of APIs that let you integrate AI into your applications with ease. In this workshop, we cover the range of APIs available to you, how to get started, and demonstrate an end-to-end example of building an AI-powered application with best practices.

Data Engineering, General Sessions,
Technology

Powering Applications and Services with DataRobot (Practical Introduction to APIs)

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On Demand

Business Analysis with DataRobot

On Demand

Understand how DataRobot can enhance your workflow and how to use DataRobot to more efficiently perform the most common types of business analyses.

Data Engineering, General Sessions, On-Demand AI Experiences,
IndustryTechnology

Business Analysis with DataRobot

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On Demand

Beyond AutoPilot

On Demand

How to level up your modeling after running Autopilot. Learn about new techniques, algorithms, and features that DataRobot makes available to improve your models.

Data Engineering, Data Preparation,
Technology

Beyond AutoPilot

View Session

Alex Measure

Economist at the Bureau of Labor Statistics Alex Measure is an economist at the Bureau of Labor Statistics and co-leader of the BLS DataScience User’s

Alexandra Porter

Director, Data and Analytics Center of Excellence – GSA Alexandra is a Director at the Data and Analytics Center of Excellence in the Technology Transformation

Alka Patel

Head of AI Ethics Policy for the Department of Defense (DoD) Joint Artificial Intelligence Center (JAIC) Alka Patel serves as the first Head of AI

Andrew Engel

Andrew Engel is the General Manager of Sports and Gaming at DataRobot. He works with DataRobot customers across sports and gaming, including several Major League

Anil (Neil) Chaudhry

Director of AI Implementations, AI Center of Excellence at GSA Mr. Chaudhry currently serves as the Director, AI implementations at GSA’s Center of Excellence. In

Anthony Scalabrino

Anthony Scalabrino is a Sales Engineer at CloudFactory where he uses his experience with AI, ML, and DL to provide end-to-end technical and non-technical solutions

Anthony Scriffignano

Senior Vice President & Chief Data Scientist, Dun & Bradstreet Anthony Scriffignano is an internationally recognized data scientist with experience spanning over35 years, in multiple

Ari Kaplan

Kaplan is a leading figure in data science, sports analytics, and business leadership. High profile roles include creating the Chicago Cubs analytics department, President of

Ben Snively

Ben Snively is a Principal Solutions Architect in Data Science for Amazon Web Services (AWS), where he specializes in building systems and solutions leveraging Big

Bennett Gebken

Senior Program Analyst – Veterans Benefits Administration (VBA) Bennett is a Senior Analyst for Security, Data, and Infrastructure with the Office of Business Integration in

Branka Panic

CEO of AI for Peace Branka Panic is the Founder and Executive Director of AI for Peace, a nonprofit ensuring artificial intelligence benefits peace, security,

Brian Rieger

Brian is an accomplished aerospace engineer, data scientist and software developer turned serial entrepreneur. He is currently Co-founder and COO of Labelbox, the industry-leading training

Carlos Rivero

Chief Data Officer at Commonwealth of Virginia Carlos is the Chief Data Officer for the Commonwealth of Virginia. Prior to his appointment, Rivero served as

Chaitan Baru

Senior Science Advisor, Convergence Accelerator – National Science Foundation (NSF) Dr. Chaitanya Baru is Senior Science Advisor in the Convergence Accelerator Office. He is there

Cheryl Ingstad

Director, Artificial Intelligence & Technology Office (AI-1) Cheryl Ingstadservesas the Director of the Artificial Intelligence & Technology Office (AITO)for the Department of Energy (DOE).AITOwas created

Chris Mattman

Division Manager, AI, Analytics and Innovative Development Organization at NASA JPL Chris Mattmann is the Division Manager of the AI, Analytics and Innovative Development Organization

Cj Rieser Ph.D.

Cj is a senior member of the IEEE women in engineering and medical communities. As the MITRE @ University of Virginia site partnership leader, Dr. Rieser directs an integrative engineering in medicine Learning

Colin Priest

Colin Priest is the VP, AI Strategy for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects.

D’vorah Graeser

Founder & CEO – KISSPatent Dr. D’vorah Graeser has supported the filing of 1000+ patent applications in 15+ industries over the course of her career.

Damian Rochman

Damian Rochman leads product management and development for CloudFactory, creating products and services that help tech teams train and augment their AI models. Damian joined

Damon Brady

Senior Director, Product Development and Programs – BAE Systems – Advanced Geospatial Solutions Damon Brady is Sr. Director for Product Development and Programs in BAE

Dan Hoffman

Dan Hoffman began service as the City of Winchester’s City Manager on September 26, 2020. In this role he directs and supervises all of the day-to-day activities of

Daniel York

Director, IT Spending Transparency, Office of Government-wide Policy – GSA Mr. York joined the Government as a Student Intern for the Department of Justice where

Dave Cook

Sr. Director for AI/ML Professional Services and the Lead AI/ML Scientist – Figure Eight Federal Dave Cook is the Sr. Director for AI/ML Professional Services

Dayo Bamikole

Dayo is a Data & AI Cloud Solutions Architect at Microsoft, supporting the U.S. Federal clientele. His focus is on creating Big Data and Machine

Doba Parushev

Director of Venture Capital – Healthworx / CareFirst BlueCross BlueShield Doba Parushev is the Director of Venture Capital at Healthworx, the corporate development and innovation

Donna Cohen Avery

Former Associate Commissioner – Massachusetts Department of Early Education and Care Donna Cohen Avery is a member of the Advisory Board at MTX Group, Inc.

Dorothy Aronson

Dorothy Aronson is NSF’s Chief Information Officer (CIO) and Chief Data Officer (CDO). She is the principal advisor to the agency’s Director and other senior

Dr. Chakib Chraibi

Chief Data Scientist, NTIS-Department of Commerce Chakib is currently Chief Data Scientist at NIST, US Department of Commerce. Formerly, he was the Dean of the

Dr. Denisha Allicock, DrPH

Public Health Strategist – MTX Group, Inc. Dr. Denisha Allicock’s main focus as part of the strategy office is to build out and inform MTX

Dragos Margineantu

AI Chief Technologist / Technical Fellow – Boeing Research & Technology Dragos Margineantu is the AI Chief Technologist and a Senior Technical Fellow with BoeingResearch

Ellery Taylor

Acting Director of the Office of Acquisition Management and Innovation Division – GSA Ellery Taylor plans, manages, directs, and coordinates procurement activities for the preaward

Gaurav Kheterpal

Chief Technology Officer – MTX Group, Inc. Gaurav Kheterpal is a seasoned Technology Executive with 20+ years of experience in areas of Enterprise product development,

Howard Levenson

Howard is an industry veteran having delivered big data and high performance computing solutions to Federal clients over his 30+ year career. He was the

Jack Jablonski

Working with a portfolio of clients, as the AI Success Manager, Jack guides the entire customer journey from onboarding to mastery and then expansion.  DataRobot wants

Jack McCarthy

CIO at the State of NJ – Judiciary Since October 2012, Jack McCarthy has been responsible for establishing strategies and direction for information technology, working

Jad Abou-Maarouf

Chief Data Officer – Fulton Bank Jad Abou-Maarouf is a transformation executive who advocates for the evolution of the human experience through data. He is

Jared Webb

Jared Webb has worked since 2016 as a member of the University of Michigan research team that used machine learning to locate Flint’s lead service

Jeff Hyacinthe

Data Scientist at GSA OCFO’s Office of Analytics, Performance, and Improvement – GSA Jeff Hyacinthe is a senior-level Data Scientist and Program Analyst at GSA

Jeremy Bash

Founder and Managing Director – Beacon Global Strategies Jeremy Bash is a founder and managing director of Beacon Global Strategies, a strategic advisory firm in

Joe Morrison

Joe works at Azavea, a B-Corporation that builds geospatial web applications aimed at creating civic, social, and environmental impact. He focuses on helping to grow

John Moore

John Moore has written about the IT industry for more than 30 years, focusing on a range of topics including distribution channels, IT consulting, systems

Jon Gacek

Head of Government, Legal & Compliance – Veritone Jon Gacek leads Veritone’s business unit providing AI-enabled solutions for the Government, Legal, and Compliance sectors. Jon

Jose Arrieta

Interim Chief Data Officer at U.S. Department of Health and Human Services (HHS) As the CIO, José provides leadership and oversight of the Department’s $6.3B

Josh Beard

Josh is a sales and business development leader who has spent his career building relationships between transformative technology companies and US government customers.  Prior to

Kassidy Kelley

Site Editor – TechTarget – SearchEnterpriseAI Kassidy Kelley is the Site Editor for SearchEnterpriseAI, specializing in reporting on data science, artificial intelligence, and machine learning.

Katy Price

MTX Group Katy has worked in innovative technology spaces for the past two decades and brings an unbridled passion and excitement based on experiences across

Kfir Yeshayahu

SVP of Products – Veritone Kfir Yeshayahu currently serves as Senior Vice President of Products at Veritone, Inc. Kfir is a passionate, customer-focused product leader,

Khalifeh Al Jadda, Ph.D.

Director of Data Science at Home Depot Khalifeh AlJadda holds a Ph.D. in computer science from the University of Georgia (UGA), with a specialization in

Kirk Borne

Principal Data Scientist and Executive Advisor at Booz Allen Kirk Borne is the Principal Data Scientist, Data Science Fellow, and an Executive Advisor at global

Krista Kinnard

Director, AI Center of Excellence, Technology Transformation Services, GSA Krista Kinnard is a Director at the Artificial Intelligence Center of Excellence in the Technology Transformation

Kyra Stewart

Team Lead, IT Vendor Management Office – GSA Kyra M. Stewart leads the effort to establish the forthcoming IT Vendor ManagementOffice (ITVMO) in GSA’s Federal

Luis Perez-Breva

Luis Perez-Breva, PhD (http://linkedin.com/in/lpbreva) is an innovator, entrepreneur, educator and the author of Innovating: A Doer’s Manifesto (http://amzn.com/0262035359 The MIT Press, 2017). He is an

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Chief Innovation Officer – MTX Group, Inc. Manish is an avid technologist with a decade of experience working in various cutting-edge technology. A biotechnologist by

Manu Sharma

Manu Sharma is an engineer, designer and entrepreneur. He is the Founder & CEO of Labelbox, the industry leading training data software that is accelerating

Maria Greicer

Maria Greicer is VP Partnerships at Keymakr, which is specializing in data collection and data creation for training of Computer Vision AI models. Maria has

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CEO – CloudFactory Mark Sears is CEO of CloudFactory, a managed platform, and workforce helping tech teams train and augment their AI models. He began

Matt Cohen

Matt is a customer-facing data scientist with DataRobot and has been a developer throughout his career. He has worked in the software industry building enterprise

Meeta Dash

VP of Product at Appen As VP Product at Appen Meeta is building a machine learning data annotation platform focused on Computer Vision, Autonomous Vehicles,

Michael Hauck

Data Scientist Senior Subject Matter Expert – DoD Dr. Michael Hauck is a senior-level consultant helping DoD with data science, advanced analytics, and artificial intelligence.

Nathaniel Gates

Nathaniel Gates is a career technology worker and entrepreneur with a focus on the Cloud Computing and Machine Learning industries. Nathaniel co-founded Alegion in 2012

Oliver Mitchell

Venture Partner – ff VC Oliver joined ff Venture Capital in 2018 as a Venture Partner. He is the founding partner at Autonomy Ventures, a

Paul Christianson

Paul Christianson helps CloudFactory clients dominate their markets by gaining a competitive edge in how they capture data to create amazing user experiences. Prior to

Phoebe Liu

Phoebe Liu is a senior data scientist with expertise in robotics and conversational AI. Previously, she was a robotics researcher in Japan, working in Hiroshi

Rajat Jain

Vice President of Fraud Risk Management at American Express Rajat Jain is the Vice President and Global Head for Identity and Authentication Strategy at American

Rajeev Sambyal

Direction, AI and Innovation, Advanced Digital Solutions – BNY Melon At Bank of New York Mellon Rajeev works across the enterprise to identify and build

Rajiv Shah

Rajiv Shah is a data scientist at DataRobot, where his primary focus is on helping customers achieve success with AI. As an expert in AI,

Randall Lin

With experience in large scale NLP on TPUs using limited labeled data and quick iterative ML experimentation tooling with Airflow and Kubernetes, Randall is currently

Robert Brown

Currently Chief Technology Officer @ USCIS.  Former Integration Architect/Cloud Solutions Developer and EID Division Chief with USCIS. Rob has 20 years of experience providing technical, managerial

Saul Morse

Chief Technology Officer – Excelsior College As the chief technology officer, Saul Morse is responsible for the overall alignment of technology solutions to serve the

Shiv Misra

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Sravan Kasarla

Chief Data Officer at Thrivent Financial Sravan Kasarla is an industry-recognized technology leader with more than 15 years of proven experience in Information Management, Business

Steve Berg

Steve Berg is a partner at Lytical Ventures – a venture fund focusing on early-stage investments in machine learning, data analytics and cyber security. The

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Tom Davenport

President’s Distinguished Professor of Information Technology and Management at Babson College Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson

Tom Suder

Founder & President- Advanced Technology Academic Research Center (ATARC) A respected thought leader in the Federal IT community, Tom Suder is the President of the

Yolanda Darricarrere

Founder of AID Trans-Network Yolanda Darricarrere has nearly 20-yrs in technical and stakeholder communications, project management, and PMO support for OCIO and IT governance shops

Yusuf Khan

Yusuf is a Data Scientist with extensive experience in R&D of AI tools. Previously, he worked with SERC where his research revolved around topic modeling,

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During the dates for the event listed above, Cognilytica offers live content available at the times listed for each session in the Agenda tab.

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