Who doesn’t love jumping into a fresh Jupyter notebook at the start of a new machine learning project? However, the romance can soon fade if you have multiple people creating new notebooks all over the place with different approaches to the same problem. Zack Akil shares pragmatic techniques and useful tools that can help you avoid common pitfalls when building ML, including tools for notebook collaboration and version control that will help prevent you and your teammates from stepping on each others’ toes as well as an iterative ML model development approach that will prevent your project from stagnating. You’ll also learn how to quickly productionize your models with machine learning frameworks like TensorFlow and scikit-learn that are trying to solve the same problem using Google ML Engine.
This session is sponsored by Google.
Zack Akil is a developer advocate for Cloud Machine Learning at Google. When he’s not teaching machine learning at Google, he likes to teach machine learning at his hands-on data science meetup, Central London Data Science Project Nights. Although he works in the cloud, most of his hobby projects look at different ways you can embed machine learning into low-power devices like Raspberry Pis and Arduinos. He also likes to have a bit of banter with his mixed tag rugby teams.
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