How to build a machine learning strategy



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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 → https://goo.gle/3aUPQd5 AI Platform → https://goo.gle/2UyIPb1 The fight against illegal deforestation with TensorFlow → https://goo.gle/38I0gLk AI Experiments → https://goo.gle/3aBsY1Y For more content like this, subscribe to the GCP Channel → https://goo.gle/34tknuO Watch more episodes of Eyes on Enterprise → https://goo.gle/2Uipf3X Product: Cloud Machine Learning Engine; fullname: Stephanie Wong, Yufeng Guo; #EyesOnEnterprise

Published by: Google Cloud Tech Published at: 4 years ago Category: علمی و تکنولوژی