Panel Discussion Archives | Tecton


Fireside Chat: Is ML a Subset or a Superset of Programming?

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 building and deploying applications. But, ML can also be considered a superset of programming, where the majority of applications being built today can be improved by infusing them with online ML predictions. Mike and Martin will share their thoughts and the implications for ML and Software Engineering teams. … Read More

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 come together to share their learnings from speaking with hundreds of leading MLOps teams. They’ll discuss their insights from identifying common patterns between these teams. … Read More

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 collaboratively as a single team. This requires adaptation on both sides – combining data science and engineering processes into a well-integrated MLOps machine. Our panel of data scientists will provide their perspective on how data engineers can support this transition and more effectively work with data science teams. … Read More

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 gap between traditional data science and the world of software engineering. In this panel discussion, we’ll discuss how high-performing ML teams are organized to build and deploy production-quality ML models with engineering best practices. … Read More

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 … Read More

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