Enabling end-to-end machine learning pipelines in real-world applications

In this episode of the Data Show , I spoke with Nick Pentreath , principal engineer at IBM. Pentreath was an early and avid user of Apache Spark, and he subsequently became a Spark committer and PMC member. Most recently his focus has been on machine learning, particularly deep learning, and he is part of a group within IBM focused on building open source tools that enable end-to-end machine learning pipelines. We had a great conversation spanning many topics, including: AI Fairness 360 (AIF360) , a set of fairness metrics for data sets and machine learning models Adversarial Robustness Toolbox (ART) , a Python library for adversarial attacks and defenses. Model Asset eXchange (MAX) , a curated and standardized collection of free and open source deep learning models. Tools for model development, governance, and operations, including MLflow , Seldon Core , and Fabric for deep learning Reinforcement learning in the enterprise, and the emergence of relevant open source tools like Ray . Related resources: “Modern Deep Learning: Tools and Techniques” —a new tutorial at the Artificial Intelligence conference in San Jose Harish Doddi on “Simplifying machine learning lifecycle management” Sharad Goel and Sam Corbett-Davies on “Why it’s hard to design fair machine learning models” “Managing risk in machine learning” : considerations for a world where ML models are becoming mission critical “The evolution and expanding utility of Ray” “Local Interpretable Model-Agnostic Explanations (LIME): An Introduction” Forough Poursabzi Sangdeh on why “It’s time for data scientists to collaborate with researchers in other disciplines”

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Enabling end-to-end machine learning pipelines in real-world applications is an episode from O'Reilly Data Show Podcast by O'Reilly Media.

This episode is 00:42:53 long.

This episode was published on Jun 20, 2019.

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