With the rise of Machine Learning came the rise of developer tools and libraries. What are they good for and what are the top ones that every data scientist and ML engineer should know. This article sheds some light on those questions.

A deep learning framework is an interface, library or a tool which allows us to build deep learning models more easily and quickly, without getting into the details of underlying algorithms. They provide a clear and concise way for defining models using a collection of pre-built and optimized components.

TensorFlow 2.0 has arrived, with a focus on ease of use, developer productivity, and scalability.

Now there’s a contest to show off your TF2 chops: The #PoweredByTF 2.0 Challenge.

Here’s a synopsis:

Developers of all ages, backgrounds, and skill levels are encouraged to submit projects. Teams may have between 1 and 6 participants. Participants are encouraged to expand the scope of an existing TensorFlow 1.x project, to migrate and continue work on a historic TensorFlow 1.x project; or to create an entirely new software solution using TensorFlow 2.0.

Keras and eager execution . Robust model deployment in production on any platform. […]

TensorFlow Lite is TensorFlow’s solution for running machine learning across resource constrained platforms. In this video, learn about the current state of TFLite, as well as the roadmap, from the point of view of four core competencies: conversion, optimization, acceleration, and usability.

With the Keras integration, and Eager Execution enabled by default, TensorFlow 2.0 is all about ease of use, and simplicity. In this episode of Coding TensorFlow, Developer Advocate Paige Bailey (Twitter: @dynamicwebpaige) shows us the tf_upgrade_v2 tool, which helps with the 2.0 transition by converting existing TensorFlow 1.12 Python scripts to TensorFlow’s 2.0 preview.