How to build a machine learning strategy

Опубликовано 25 марта 2020, 14:00

In this episode of Eyes on Enterprise, Stephanie Wong invites Yufueng Guo, Developer Advocate for machine learning, to talk about how enterprises can incorporate ML into their environments, build workflows, and apply them to real world scenarios. Specifically, they discuss how to frame ML as descriptive, predictive, or prescriptive problems, and when to use tooling like Tensorflow, Keras, Scikit Learn, and Pytorch.

Follow Stephanie Wong on Twitter → @swongful

Time Markers:
0:00 Intro
0:34 Trends in ML
1:32 AI vs. ML
2:57 Building an ML workflow
10:31 ML tools and developer experience
11:33 Approaching ML by problem type
13:45 Processing in the cloud vs. on-prem
17:51 Google Cloud tools
20:12 Real world use cases
21:57 Takeaways

The 7 Steps of Machine Learning → goo.gle/3aUPQd5
AI Platform → goo.gle/2UyIPb1
The fight against illegal deforestation with TensorFlow → goo.gle/38I0gLk
AI Experiments → goo.gle/3aBsY1Y

For more content like this, subscribe to the GCP Channel → goo.gle/34tknuO
Watch more episodes of Eyes on Enterprise → goo.gle/2Uipf3X

Product: Cloud Machine Learning Engine; fullname: Stephanie Wong, Yufeng Guo;

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