Join Mike and Martin in this fireside chat where they’ll discuss whether ML should be considered a subset or a superset of programming. ML can be considered a specialized subset of programming, which introduces unique requirements on the process of …
Panel: Common Patterns of the World’s Most Successful ML Teams
There’s a lot we can learn simply by observing the most successful ML teams in the world: how they operate, which technology stack they use, which skill sets they value, and which processes they implement. In this panel, MLOps thought leaders will …
Panel: What Do Engineers Not Get About Working with Data Scientists?
ML is increasingly making its way into production to power customer-facing applications and business processes. This transition from batch to operational ML raises new organizational challenges. Data scientists and engineers now have to work …
Panel: Building High-Performance ML Teams
As Machine Learning moves to production, ML teams have to evolve into high-performing engineering teams. Data science is still a central role, but no longer sufficient. We now need new functions (e.g. MLOps Engineers) and new processes to bridge the …
Panel: Challenges of Operationalizing ML
Our panel discussion will focus on the main challenges of building and deploying ML applications. We’ll discuss common pitfalls, development best practices, and the latest trends in tooling to effectively operationalize ML
