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Learn MLOps from These GitHub Repositories

As machine learning (ML) becomes more prevalent, MLOps (ML Operations) is becoming increasingly important. MLOps encompasses the processes and tools used to deploy, monitor, and maintain ML models in production. It requires tight collaboration between data scientists, DevOps engineers, and IT teams. To help you get started on your MLOps journey, here is a list of 5 GitHub repositories that offer MLOps resources:

1. MLOps-best-practices

The MLOps-best-practices repository provides an excellent overview of best practices and tools for MLOps, as well as a comprehensive guide to setting up a MLOps pipeline using TensorFlow, Keras, and Kubeflow.

2. MLOps-reference-architectures

This repository provides reference architectures and implementation instructions for building and deploying MLOps pipelines with Docker, Kubernetes, and Jenkins. It also offers guidance on using popular MLOps tools like MLFlow, Seldon, and TFX.

3. MLOps-playground

The MLOps-playground repository provides a set of ready-made MLOps environments in the form of pre-configured Docker containers. These containers provide a quick and easy way to get started with MLOps and experiment with different tools and techniques.

4. MLOps-monitoring

This repository provides a comprehensive framework for monitoring the performance of ML models in production. It outlines various metrics that should be monitored, such as accuracy, latency, memory usage, and resource utilization.

5. MLOps-security

The MLOps-security repository provides a guide to implementing secure MLOps practices. It outlines principles for securing ML models and data, as well as best practices for deploying and managing ML models in production.

These GitHub repositories can be a great starting point for learning MLOps. They provide an overview of MLOps principles, references architectures, and step-by-step instructions on building and deploying MLOps pipelines. So, get started today and deepen your knowledge of MLOps!