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Machine Learning Operations (MLOps)

MLOps streamlines and automates the end-to-end machine learning lifecycle, covering model development, deployment, monitoring, and maintenance to ensure efficient and reliable ML systems.

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While the above courses may not be accessible for free, you can seek financial aid to enroll in them.


  • Cloud GPUs (opens in a new tab) by The Full Stack, In neural network training, hardware acceleration—typically GPUs—is crucial. The platform organizes cloud GPU vendor pricing into sortable tables, categorizing offerings into GPU Cloud Servers, which are long-running and possibly pre-emptible, and Serverless GPUs that scale to zero when inactive, similar to AWS Lambda or Google Cloud Function.

  • MLOps Org (opens in a new tab) is a comprehensive MLOps resource, covering core principles, recommended books, frameworks, tools, and best practices. It discusses fundamental MLOps principles, references key frameworks and communities, and highlights crucial components for MLOps success. Additionally, it introduces the MLOps Stack Canvas for structured ML application development and maintenance.



  • Google Cloud Developer Cheat Sheet (opens in a new tab) is a quick reference guide for developers on Google Cloud, featuring every product, feature, and service in the Google Cloud family in depth. Google Cloud's suite includes compute, storage, networking, big data, AI/ML, IoT, management, security, and developer tools. (poster on GitHub)

  • Weights & Biases Docs (opens in a new tab): Weights & Biases is the machine learning platform that enables developers to accelerate model development. With just a few lines of code, W&B lets you track, compare, and visualize your ML models. This simplifies experiment tracking for better analysis and result reproducibility.