Awesome MLOps
by visenger
This is an awesome list of references for MLOps - Machine Learning Operations. It provides a curated collection of resources to help you design, train, and run machine learning systems effectively.
Last updated: N/A
What is Awesome MLOps?
Awesome MLOps is a curated list of resources, including articles, books, courses, communities, and tools, related to Machine Learning Operations (MLOps). It aims to provide a comprehensive collection of references for individuals and teams looking to implement and improve their MLOps practices.
How to use Awesome MLOps?
Use this list as a starting point for exploring various aspects of MLOps. Browse the different categories in the table of contents to find resources relevant to your specific needs, whether it's core MLOps principles, workflow management, feature stores, model deployment, monitoring, or infrastructure.
Key features of Awesome MLOps
Curated list of MLOps resources
Categorized by topic for easy navigation
Links to articles, books, courses, communities, and tools
Covers the entire MLOps lifecycle
Regularly updated with new and relevant content
Use cases of Awesome MLOps
Setting up an MLOps pipeline
Improving model deployment and monitoring
Selecting the right tools for your MLOps stack
Learning about best practices for MLOps
Building a strong MLOps community within your organization
FAQ from Awesome MLOps
What is MLOps?
What is MLOps?
MLOps (Machine Learning Operations) is a set of practices that aims to automate and streamline the machine learning lifecycle, from development to deployment and maintenance.
Why is MLOps important?
Why is MLOps important?
MLOps helps organizations to deploy and manage machine learning models more efficiently, reliably, and at scale, leading to faster innovation and better business outcomes.
What are the key components of an MLOps pipeline?
What are the key components of an MLOps pipeline?
Key components include data engineering, feature engineering, model training, model validation, model deployment, and model monitoring.
What are some popular MLOps tools?
What are some popular MLOps tools?
Popular tools include MLflow, Kubeflow, Feast, Tecton, Seldon Core, and many others. The choice of tools depends on the specific needs and requirements of your project.
How can I contribute to this list?
How can I contribute to this list?
You can contribute by submitting a pull request with your suggestions for new resources or improvements to existing ones. Please ensure that your contributions are relevant and high-quality.