Awesome GitHub RepositoriesMLOps

This project is a curated directory of software repositories specifically selected to help newcomers make their first open-source contributions. It serves as a collaborative knowledge base that aggregates entry-level development opportunities, providing a structured path for novice developers to practice version control and engage with active software communities. The repository distinguishes itself through a community-driven model where project listings are populated and verified by external contributors. This distributed peer review process ensures the directory remains current, while the u

Kubeflow is a Kubernetes machine learning platform and containerized toolkit designed to orchestrate the entire machine learning lifecycle. It functions as an MLOps workflow orchestrator and infrastructure layer for building, training, and deploying models within containerized environments. The project provides specialized infrastructure for scaling compute resources and managing GPU workloads for large-scale distributed training. It automates the transition of models from experimental development to production through workflow orchestration and model deployment services. The platform covers

Feast is a machine learning feature store and MLOps data infrastructure layer. It provides a centralized system for managing and serving features across offline training and online production environments, utilizing an online feature serving layer for low-latency retrieval. The project centers on a feature registry that acts as a central catalog for defining, governing, and discovering feature services. It employs a unified data access layer to decouple feature retrieval from physical storage and includes a point-in-time data generator to create historically accurate training datasets that pr

PostgresML is a machine learning database extension for PostgreSQL that integrates model training and inference directly into the database. It functions as an in-database AI platform and vector database, enabling the execution of large language models and natural language processing tasks on stored records without exporting data to external services. The system distinguishes itself by utilizing GPU acceleration to minimize latency during model predictions and employing a hybrid storage engine that maintains relational data alongside high-dimensional vectors. It allows for the building and fin

nlp-recipes is a collection of implementation guides and reference templates for applying natural language processing techniques to real-world tasks. It provides standardized workflows and code examples for developing NLP pipelines, from dataset preparation and model training to performance evaluation. The project focuses on the practical application of transformer-based models, offering patterns for fine-tuning pretrained architectures for tasks such as text classification, named entity recognition, and question answering. It also includes a toolkit for model interpretability, allowing users

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

This is a reference guide for designing, deploying, and maintaining production-ready machine learning systems, grounded in MLOps best practices. It covers the complete machine learning lifecycle, from system design and workflow planning through to deployment and ongoing maintenance, with a focus on reliability, scalability, and maintainability as business requirements evolve. The guide provides an architecture reference for establishing shared ML infrastructure, including model registries and feature stores that standardize asset reuse across teams. It details pipeline automation through conf

Deepchecks este un framework de validare a modelelor de machine learning și o bibliotecă de testare MLOps. Servește ca o suită de calitate a datelor AI și evaluator de performanță conceput pentru a verifica integritatea și performanța modelelor și seturilor de date, de la cercetare până la producție. Proiectul funcționează ca un instrument de monitorizare a modelelor pentru urmărirea derivei datelor (data drift) și a degradării performanței în mediile de producție. Permite crearea de suite de validare personalizate și utilizează o arhitectură de verificări pluggable pentru a automatiza testele de calitate în cadrul pipeline-urilor de integrare continuă (CI). Framework-ul acoperă o gamă largă de capabilități, inclusiv verificarea integrității datelor, detectarea derivei bazată pe distribuție și compararea versiunilor de modele. Oferă analiză specializată pentru computer vision și procesarea limbajului natural (NLP), alături de instrumente de raportare care transformă metricile de validare în rapoarte vizuale interactive. Sistemul suportă implementarea on-premises pentru a menține confidențialitatea datelor și controlul infrastructurii.