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ML Design Patterns for Data Engineers

Posted Mar 29
# Data engineering
# Systems and Architecture
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SPEAKER
Valliappa Lakshmanan
Valliappa Lakshmanan
Valliappa Lakshmanan
Director @ Google

Lak is the Director for Data Analytics and AI Solutions on Google Cloud, where his team builds software solutions for problems across industries. He also founded GoogleÕs Advanced Solutions Lab ML Immersion program and is the author of three OÕReilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA.

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Lak is the Director for Data Analytics and AI Solutions on Google Cloud, where his team builds software solutions for problems across industries. He also founded GoogleÕs Advanced Solutions Lab ML Immersion program and is the author of three OÕReilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA.

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SUMMARY

As machine learning moves from being a research discipline to a software one, it is useful to catalog tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover three patterns that are useful in creating large-scale and resilient data and ML pipelines. These patterns provide a way to apply hard-won knowledge from hundreds of ML experts to your own projects.

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