Analyze billions of records faster with RAPIDS & Nvidia GPUs on Google Cloud’s AI Platform Notebooks.

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My superpower toolkit: TFRecorder, TensorFlow Cloud, AI Platform Predictions and Weights & Biases

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  • Need to scale your model training? Learn AllReduce and multi-node distributed architectures
  • Need to deploy your models? Learn Kubernetes, TFServing, quantization, and API management
  • Need to track pipelines? Set up a metadata database, learn docker, and become a DevOps engineer


A Framework to assess GPUs for your team

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Summary

  • CatBoost, an OSS gradient boosting framework , is the new kid on the block with intriguing benchmark results on model quality and training/serving speeds with CPUs & GPUs
  • Catboost is simple to run on Google Cloud’s AI Platform for both the Notebook & Training services. GitHub examples to accompany this post HERE
  • While AI Platform Training only lists TensorFlow, XGBoost, & SKLearn as officially supported hosted frameworks, CatBoost works seamlessly by including it in the setup.py file during training job submission

CatBoost for Gradient Boosting

My focus the last few years has been heavily skewed towards deep learning & neural networks but this weekend…

Mikhail Chrestkha

Data Science & Machine Learning @ Google Cloud mchrestkha.github.io

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