Awesome GitHub RepositoriesMLOps and Deployment

This project is a structured educational program and comprehensive training curriculum designed to teach the end-to-end lifecycle of machine learning models. It serves as a resource for engineers to master the transition of data science projects from development into reliable, production-ready systems. The curriculum focuses on the practical application of engineering best practices, emphasizing the orchestration of complex data processing and training sequences. It provides instruction on building repeatable workflows, managing experiment metadata, and implementing infrastructure automation

This project is a comprehensive collection of machine learning educational resources, featuring a Python-based curriculum, study guides for deep learning, and a specialized knowledge base for machine learning operations. It provides structured learning paths that guide users from foundational programming through to advanced neural network implementations. The repository focuses on interactive learning by providing a directory of executable notebooks and cloud-hosted experiments. It maps theoretical research papers and textbooks to practical code implementations and maintains a curated directo

PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp

This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures. The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine l

BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package, deploy, and scale AI models as production-ready REST APIs. It functions as an AI model lifecycle manager and an inference graph orchestrator, enabling the chaining of multiple models and custom logic into complex pipelines for advanced task sequences. The framework distinguishes itself through a dynamic batching engine that optimizes GPU throughput and an artifact-based packaging system that bundles model weights and dependencies into immutable archives for consistent deployment. It

ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving. The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r

Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma

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

ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

Ce projet est une bibliothèque d'intégration AWS pandas et un framework de pipeline de données conçu pour simplifier le mouvement et la transformation des données entre la mémoire locale et les services de stockage et d'analyse AWS. Il fonctionne comme une boîte à outils de lac de données cloud et un gestionnaire de fichiers de stockage, permettant aux utilisateurs de lire, écrire et transformer des données structurées à travers divers environnements cloud. La bibliothèque se distingue comme un orchestrateur de calcul distribué capable de gérer des clusters dans des environnements tels qu'EMR pour traiter des jeux de données qui dépassent les limites de mémoire d'une seule machine. Elle fournit également des capacités spécialisées pour gérer des indices vectoriels et effectuer des recherches de similarité au sein de buckets de stockage cloud. Sa surface de capacité plus large couvre l'ETL de base de données cloud pour des services comme DynamoDB, RDS et Timestream, ainsi que la gestion de catalogue de données cloud via AWS Glue. Elle prend en charge l'analyse de données sans serveur via Athena et Redshift, et fournit des utilitaires pour gérer les objets S3, indexer des documents dans OpenSearch et analyser les journaux CloudWatch.

Polyaxon is a Kubernetes-native machine learning orchestration platform and MLOps pipeline orchestrator. It serves as a control plane for managing distributed deep learning workloads, automated machine learning pipelines, and experiment tracking. The platform distinguishes itself through specialized services for distributed training management, including MPI-based coordination for PyTorch and TensorFlow. It provides an automated hyperparameter optimization service utilizing Bayesian, random, and grid search algorithms, alongside managed interactive AI workspaces for launching Jupyter notebook

Hopsworks - Data-Intensive AI platform with a Feature Store

Train and Deploy an ML REST API to predict crypto prices, in 10 steps

Serverless proxy for Spark cluster