Session Type: Live

Welcome to ML Lifecycle Conference and ‘What is the Machine Learning Lifecycle’?

In this opening session we’ll share some insights into what will be presented throughout the conference, including both live sessions as well as on-demand sessions. We will also answer the question: What is the Machine Learning Lifecycle? And also discuss why is there an event focused entirely around the whole Machine Learning lifecycle, as well as touch upon the five core topics areas of this event

Zorroa Ask an Expert Session

Join us for an Ask an Expert session with Zorroa’s data science engineer, principal architect, and product manager. The team will address questions around the emergence of no-code ML and how their platform is enabling ML experimentation and rapid development without requiring dedicated engineering teams.

ML never works in isolation!

Machine Learning is part of a larger need in an organization. In this information revolution, where we are inundated with information, ML needs to be leveraged to sort / prioritize / handle realistic business problems. Agency problems are often process focused, more regulated each day. The focus of ML has to be one that addresses …

ML never works in isolation! Read More »

Better interoperability and scalability with no-code ML

How do you stand up successful ML projects in a world where ML implementation—or even experimentation—is still cost-prohibitive, unpredictable, and has an 87% failure rate? How can teams ensure connectivity across cloud-based tools and workflows as projects scale? In this session, Zorroa will discuss how today’s no-code ML workflows are supporting software connectivity and enabling …

Better interoperability and scalability with no-code ML Read More »

Are you using Airflow or similar SW for ML pipelining? You’re doing it all wrong.

There are many that try to bridge the gap between the iterative, incremental work done in Research and the integrated, routinely automated workflows of modern software development. This manifests itself in the DevOps pipelining tools that ML practitioners try to “jam” for their research needs. From our experience working with groundbreaking companies, we can surmise …

Are you using Airflow or similar SW for ML pipelining? You’re doing it all wrong. Read More »

AI in Healthcare

In this panel, we hear how leading healthcare industry professionals are tackling the challenges of healthcare with AI.

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