2021 August Archives | Page 3 of 3 | Tecton


Building Malleable ML Systems through Measurement, Monitoring & Maintenance

Machine learning systems are now easier to build than ever, but they still don’t perform as well as we would hope on real applications. I’ll explore a simple idea in this talk: if ML systems were more malleable and could be maintained like software, we might build better systems. I’ll discuss an immediate bottleneck towards building more malleable ML systems: the evaluation pipeline. I’ll describe the need for finer-grained performance measurement and monitoring, the opportunities paying attention to this area could open up in maintaining ML systems, and some of the tools that I’m building (with great collaborators) in the Robustness Gym and Meerkat projects to close this gap. Read More

ML Observability: Critical Piece of the ML Stack

As more and more machine learning models are deployed into production, it is imperative we have better observability tools to monitor, troubleshoot, and explain their decisions. In this talk, Aparna Dhinakaran, Co-Founder, CPO of Arize AI (Ex-Uber Machine Learning), will discuss the state of the commonly seen ML Production monitoring and its challenges. She will focus on how to use statistical distance checks to monitor features and model output in production, how to analyze the changes effects on models and how to use explainability techniques to determine if issues are model or data related. Read More

Streaming Architecture with Kafka, Materialize, dbt, and Tecton

Drizly is building out our Data Science stack and streaming infrastructure to match the success we’ve had with the modern data stack on BI. We are currently standing up our architecture using Kafka, Materialize, and dbt. We are planning on adding Tecton into this stack as our feature store for Data Science. We can show our current plans for infrastructure, talk about the pros of this stack, and touch on how this opens up future possibilities with Tecton as our feature store. Read More

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