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Google AutoML Tables vs Leaf: What are the differences?
Developers describe Google AutoML Tables as "Automatically build and deploy machine learning models on structured data". Enables your entire team of data scientists, analysts, and developers to automatically build and deploy machine learning models on structured data at massively increased speed and scale. On the other hand, Leaf is detailed as "Machine learning framework in Rust". Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack.
Google AutoML Tables and Leaf belong to "Machine Learning Tools" category of the tech stack.
Leaf is an open source tool with 5.41K GitHub stars and 270 GitHub forks. Here's a link to Leaf's open source repository on GitHub.