This repository serves as a comprehensive, curated list of references for Machine Learning Operations (MLOps). It is designed for engineers, researchers, and practitioners seeking to understand and implement best practices in operationalizing machine learning models. The collection aims to provide a structured overview of the MLOps landscape, covering core concepts, tools, communities, and best practices.

How It Works

The project functions as an extensive, categorized directory of external resources. It meticulously organizes links to articles, books, courses, talks, papers, and community platforms related to MLOps. This curated approach allows users to navigate the complex MLOps domain by providing pointers to authoritative and relevant information across various sub-disciplines.

Quick Start & Requirements

This repository is a curated list of resources and does not contain executable code. Therefore, there are no installation or running requirements. Users can directly browse the provided links to access the referenced materials.

Highlighted Details

• Broad Categorization: The list is meticulously organized into over 20 distinct categories, including MLOps Core, Communities, Books, Articles, Workflow Management, Feature Stores, Data Engineering (DataOps), Model Deployment and Serving, Testing, Monitoring, Infrastructure, Papers, Talks, Existing ML Systems, Software Engineering, Product Management for ML/AI, Economics of ML/AI, and Model Governance.
• Extensive Resource Collection: It includes a vast array of resources, ranging from foundational books and academic papers to practical guides, online courses, and community forums, offering a deep dive into various facets of MLOps.
• Community Integration: Several MLOps communities and their associated resources (e.g., MLOps.community, DataTalks.Club) are highlighted, fostering a sense of community and collaboration.

Maintenance & Community

The repository is curated by Dr. Larysa Visengeriyeva. It links to several active MLOps communities, including MLOps.community and the CDF SIG-MLOps, suggesting a connection to a broader MLOps ecosystem. The presence of numerous community links indicates an active and growing field.

Licensing & Compatibility

No specific license information is provided within the README. As a curated list of external links, compatibility is determined by the licenses of the linked resources.

Limitations & Caveats

As a curated list, the repository's value is dependent on the ongoing maintenance and relevance of its external links. The sheer volume of resources may be overwhelming for newcomers, and the absence of direct code or tools means it serves purely as a pointer to information rather than a practical implementation.