A curated list of awesome MLOps tools.

Inspired by awesome-python.

• Awesome MLOps
  • AutoML
  • CI/CD for Machine Learning
  • Cron Job Monitoring
  • Data Catalog
  • Data Enrichment
  • Data Exploration
  • Data Management
  • Data Processing
  • Data Validation
  • Data Visualization
  • Drift Detection
  • Feature Engineering
  • Feature Store
  • Hyperparameter Tuning
  • Knowledge Sharing
  • Machine Learning Platform
  • Model Fairness and Privacy
  • Model Interpretability
  • Model Lifecycle
  • Model Serving
  • Model Testing & Validation
  • Optimization Tools
  • Simplification Tools
  • Visual Analysis and Debugging
  • Workflow Tools
• Resources
  • Articles
  • Books
  • Events
  • Other Lists
  • Podcasts
  • Slack
  • Websites
• Contributing

Tools for performing AutoML.

• AutoGluon - Automated machine learning for image, text, tabular, time-series, and multi-modal data.
• AutoKeras - AutoKeras goal is to make machine learning accessible for everyone.
• AutoPyTorch - Automatic architecture search and hyperparameter optimization for PyTorch.
• AutoSKLearn - Automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
• EvalML - A library that builds, optimizes, and evaluates ML pipelines using domain-specific functions.
• FLAML - Finds accurate ML models automatically, efficiently and economically.
• H2O AutoML - Automates ML workflow, which includes automatic training and tuning of models.
• MindsDB - AI layer for databases that allows you to effortlessly develop, train and deploy ML models.
• MLBox - MLBox is a powerful Automated Machine Learning python library.
• Model Search - Framework that implements AutoML algorithms for model architecture search at scale.
• NNI - An open source AutoML toolkit for automate machine learning lifecycle.

Tools for performing CI/CD for Machine Learning.

• ClearML - Auto-Magical CI/CD to streamline your ML workflow.
• CML - Open-source library for implementing CI/CD in machine learning projects.
• KitOps – Open source MLOps project that eases model handoffs between data scientist and DevOps.

Tools for monitoring cron jobs (recurring jobs).

• Cronitor - Monitor any cron job or scheduled task.
• HealthchecksIO - Simple and effective cron job monitoring.

Tools for data cataloging.

• Amundsen - Data discovery and metadata engine for improving the productivity when interacting with data.
• Apache Atlas - Provides open metadata management and governance capabilities to build a data catalog.
• CKAN - Open-source DMS (data management system) for powering data hubs and data portals.
• DataHub - LinkedIn's generalized metadata search & discovery tool.
• Magda - A federated, open-source data catalog for all your big data and small data.
• Metacat - Unified metadata exploration API service for Hive, RDS, Teradata, Redshift, S3 and Cassandra.
• OpenMetadata - A Single place to discover, collaborate and get your data right.

Tools and libraries for data enrichment.

• Snorkel - A system for quickly generating training data with weak supervision.
• Upgini - Enriches training datasets with features from public and community shared data sources.

Tools for performing data exploration.

• Apache Zeppelin - Enables data-driven, interactive data analytics and collaborative documents.
• BambooLib - An intuitive GUI for Pandas DataFrames.
• DataPrep - Collect, clean and visualize your data in Python.
• Google Colab - Hosted Jupyter notebook service that requires no setup to use.
• Jupyter Notebook - Web-based notebook environment for interactive computing.
• JupyterLab - The next-generation user interface for Project Jupyter.
• Jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts.
• Pandas Profiling - Create HTML profiling reports from pandas DataFrame objects.
• Polynote - The polyglot notebook with first-class Scala support.

Tools for performing data management.

• Arrikto - Dead simple, ultra fast storage for the hybrid Kubernetes world.
• BlazingSQL - A lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.
• Delta Lake - Storage layer that brings scalable, ACID transactions to Apache Spark and other engines.
• Dolt - SQL database that you can fork, clone, branch, merge, push and pull just like a git repository.
• Dud - A lightweight CLI tool for versioning data alongside source code and building data pipelines.
• DVC - Management and versioning of datasets and machine learning models.
• Git LFS - An open source Git extension for versioning large files.
• Hub - A dataset format for creating, storing, and collaborating on AI datasets of any size.
• Intake - A lightweight set of tools for loading and sharing data in data science projects.
• lakeFS - Repeatable, atomic and versioned data lake on top of object storage.
• Marquez - Collect, aggregate, and visualize a data ecosystem's metadata.
• Milvus - An open source embedding vector similarity search engine powered by Faiss, NMSLIB and Annoy.
• Pinecone - Managed and distributed vector similarity search used with a lightweight SDK.
• Qdrant - An open source vector similarity search engine with extended filtering support.
• Quilt - A self-organizing data hub with S3 support.

Tools related to data processing and data pipelines.

• Airflow - Platform to programmatically author, schedule, and monitor workflows.
• Azkaban - Batch workflow job scheduler created at LinkedIn to run Hadoop jobs.
• Dagster - A data orchestrator for machine learning, analytics, and ETL.
• Hadoop - Framework that allows for the distributed processing of large data sets across clusters.
• OpenRefine - Power tool for working with messy data and improving it.
• Spark - Unified analytics engine for large-scale data processing.

Tools related to data validation.

• Cerberus - Lightweight, extensible data validation library for Python.
• Cleanlab - Python library for data-centric AI and machine learning with messy, real-world data and labels.
• Great Expectations - A Python data validation framework that allows to test your data against datasets.
• JSON Schema - A vocabulary that allows you to annotate and validate JSON documents.
• TFDV - An library for exploring and validating machine learning data.

Tools for data visualization, reports and dashboards.

• Count - SQL/drag-and-drop querying and visualisation tool based on notebooks.
• Dash - Analytical Web Apps for Python, R, Julia, and Jupyter.
• Data Studio - Reporting solution for power users who want to go beyond the data and dashboards of GA.
• Facets - Visualizations for understanding and analyzing machine learning datasets.
• Grafana - Multi-platform open source analytics and interactive visualization web application.
• Lux - Fast and easy data exploration by automating the visualization and data analysis process.
• Metabase - The simplest, fastest way to get business intelligence and analytics to everyone.
• Redash - Connect to any data source, easily visualize, dashboard and share your data.
• SolidUI - AI-generated visualization prototyping and editing platform, support 2D and 3D models.
• Superset - Modern, enterprise-ready business intelligence web application.
• Tableau - Powerful and fastest growing data visualization tool used in the business intelligence industry.

Tools and libraries related to drift detection.

• Alibi Detect - An open source Python library focused on outlier, adversarial and drift detection.
• Frouros - An open source Python library for drift detection in machine learning systems.
• TorchDrift - A data and concept drift library for PyTorch.

Tools and libraries related to feature engineering.

• Feature Engine - Feature engineering package with SKlearn like functionality.
• Featuretools - Python library for automated feature engineering.
• TSFresh - Python library for automatic extraction of relevant features from time series.

Feature store tools for data serving.

• Butterfree - A tool for building feature stores. Transform your raw data into beautiful features.
• ByteHub - An easy-to-use feature store. Optimized for time-series data.
• Feast - End-to-end open source feature store for machine learning.
• Feathr - An enterprise-grade, high performance feature store.
• Featureform - A Virtual Feature Store. Turn your existing data infrastructure into a feature store.
• Tecton - A fully-managed feature platform built to orchestrate the complete lifecycle of features.

Tools and libraries to perform hyperparameter tuning.

• Advisor - Open-source implementation of Google Vizier for hyper parameters tuning.
• Hyperas - A very simple wrapper for convenient hyperparameter optimization.
• Hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python.
• Katib - Kubernetes-based system for hyperparameter tuning and neural architecture search.
• KerasTuner - Easy-to-use, scalable hyperparameter optimization framework.
• Optuna - Open source hyperparameter optimization framework to automate hyperparameter search.
• Scikit Optimize - Simple and efficient library to minimize expensive and noisy black-box functions.
• Talos - Hyperparameter Optimization for TensorFlow, Keras and PyTorch.
• Tune - Python library for experiment execution and hyperparameter tuning at any scale.

Tools for sharing knowledge to the entire team/company.

• Knowledge Repo - Knowledge sharing platform for data scientists and other technical professions.
• Kyso - One place for data insights so your entire team can learn from your data.

Complete machine learning platform solutions.

• aiWARE - aiWARE helps MLOps teams evaluate, deploy, integrate, scale & monitor ML models.
• Algorithmia - Securely govern your machine learning operations with a healthy ML lifecycle.
• Allegro AI - Transform ML/DL research into products. Faster.
• Bodywork - Deploys machine learning projects developed in Python, to Kubernetes.
• CNVRG - An end-to-end machine learning platform to build and deploy AI models at scale.
• DAGsHub - A platform built on open source tools for data, model and pipeline management.
• Dataiku - Platform democratizing access to data and enabling enterprises to build their own path to AI.
• DataRobot - AI platform that democratizes data science and automates the end-to-end ML at scale.
• Domino - One place for your data science tools, apps, results, models, and knowledge.
• Edge Impulse - Platform for creating, optimizing, and deploying AI/ML algorithms for edge devices.
• envd - Machine learning development environment for data science and AI/ML engineering teams.
• FedML - Simplifies the workflow of federated learning anywhere at any scale.
• Gradient - Multicloud CI/CD and MLOps platform for machine learning teams.
• H2O - Open source leader in AI with a mission to democratize AI for everyone.
• Hopsworks - Open-source platform for developing and operating machine learning models at scale.
• Iguazio - Data science platform that automates MLOps with end-to-end machine learning pipelines.
• Katonic - Automate your cycle of intelligence with Katonic MLOps Platform.
• Knime - Create and productionize data science using one easy and intuitive environment.
• Kubeflow - Making deployments of ML workflows on Kubernetes simple, portable and scalable.
• LynxKite - A complete graph data science platform for very large graphs and other datasets.
• ML Workspace - All-in-one web-based IDE specialized for machine learning and data science.
• MLReef - Open source MLOps platform that helps you collaborate, reproduce and share your ML work.
• Modzy - Deploy, connect, run, and monitor machine learning (ML) models in the enterprise and at the edge.
• Neu.ro - MLOps platform that integrates open-source and proprietary tools into client-oriented systems.
• Omnimizer - Simplifies and accelerates MLOps by bridging the gap between ML models and edge hardware.
• Pachyderm - Combines data lineage with end-to-end pipelines on Kubernetes, engineered for the enterprise.
• Polyaxon - A platform for reproducible and scalable machine learning and deep learning on kubernetes.
• Sagemaker - Fully managed service that provides the ability to build, train, and deploy ML models quickly.
• SAS Viya - Cloud native AI, analytic and data management platform that supports the analytics life cycle.
• Sematic - An open-source end-to-end pipelining tool to go from laptop prototype to cloud in no time.
• SigOpt - A platform that makes it easy to track runs, visualize training, and scale hyperparameter tuning.
• TrueFoundry - A Cloud-native MLOps Platform over Kubernetes to simplify training and serving of ML Models.
• Valohai - Takes you from POC to production while managing the whole model lifecycle.

Tools for performing model fairness and privacy in production.

• AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models.
• Fairlearn - A Python package to assess and improve fairness of machine learning models.
• Opacus - A library that enables training PyTorch models with differential privacy.
• TensorFlow Privacy - Library for training machine learning models with privacy for training data.

Tools for performing model interpretability/explainability.

• Alibi - Open-source Python library enabling ML model inspection and interpretation.
• Captum - Model interpretability and understanding library for PyTorch.
• ELI5 - Python package which helps to debug machine learning classifiers and explain their predictions.
• InterpretML - A toolkit to help understand models and enable responsible machine learning.
• LIME - Explaining the predictions of any machine learning classifier.
• Lucid - Collection of infrastructure and tools for research in neural network interpretability.
• SAGE - For calculating global feature importance using Shapley values.
• SHAP - A game theoretic approach to explain the output of any machine learning model.

Tools for managing model lifecycle (tracking experiments, parameters and metrics).

• Aim - A super-easy way to record, search and compare 1000s of ML training runs.
• Cascade - Library of ML-Engineering tools for rapid prototyping and experiment management.
• Comet - Track your datasets, code changes, experimentation history, and models.
• Guild AI - Open source experiment tracking, pipeline automation, and hyperparameter tuning.
• Keepsake - Version control for machine learning with support to Amazon S3 and Google Cloud Storage.
• Losswise - Makes it easy to track the progress of a machine learning project.
• Mlflow - Open source platform for the machine learning lifecycle.
• ModelDB - Open source ML model versioning, metadata, and experiment management.
• Neptune AI - The most lightweight experiment management tool that fits any workflow.
• Sacred - A tool to help you configure, organize, log and reproduce experiments.
• Weights and Biases - A tool for visualizing and tracking your machine learning experiments.

Tools for serving models in production.

• Banana - Host your ML inference code on serverless GPUs and integrate it into your app with one line of code.
• Beam - Develop on serverless GPUs, deploy highly performant APIs, and rapidly prototype ML models.
• BentoML - Open-source platform for high-performance ML model serving.
• BudgetML - Deploy a ML inference service on a budget in less than 10 lines of code.
• Cog - Open-source tool that lets you package ML models in a standard, production-ready container.
• Cortex - Machine learning model serving infrastructure.
• Geniusrise - Host inference APIs, bulk inference and fine tune text, vision, audio and multi-modal models.
• Gradio - Create customizable UI components around your models.
• GraphPipe - Machine learning model deployment made simple.
• Hydrosphere - Platform for deploying your Machine Learning to production.
• KFServing - Kubernetes custom resource definition for serving ML models on arbitrary frameworks.
• LocalAI - Drop-in replacement REST API that’s compatible with OpenAI API specifications for inferencing.
• Merlin - A platform for deploying and serving machine learning models.
• MLEM - Version and deploy your ML models following GitOps principles.
• Opyrator - Turns your ML code into microservices with web API, interactive GUI, and more.
• PredictionIO - Event collection, deployment of algorithms, evaluation, querying predictive results via APIs.
• Quix - Serverless platform for processing data streams in real-time with machine learning models.
• Rune - Provides containers to encapsulate and deploy EdgeML pipelines and applications.
• Seldon - Take your ML projects from POC to production with maximum efficiency and minimal risk.
• Streamlit - Lets you create apps for your ML projects with deceptively simple Python scripts.
• TensorFlow Serving - Flexible, high-performance serving system for ML models, designed for production.
• TorchServe - A flexible and easy to use tool for serving PyTorch models.
• Triton Inference Server - Provides an optimized cloud and edge inferencing solution.
• Vespa - Store, search, organize and make machine-learned inferences over big data at serving time.

Tools for testing and validating models.

• Deepchecks - Open-source package for validating ML models & data, with various checks and suites.
• Starwhale - An MLOps/LLMOps platform for model building, evaluation, and fine-tuning.
• Trubrics - Validate machine learning with data science and domain expert feedback.

Optimization tools related to model scalability in production.

• Accelerate - A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.
• Dask - Provides advanced parallelism for analytics, enabling performance at scale for the tools you love.
• DeepSpeed - Deep learning optimization library that makes distributed training easy, efficient, and effective.
• Fiber - Python distributed computing library for modern computer clusters.
• Horovod - Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
• Mahout - Distributed linear algebra framework and mathematically expressive Scala DSL.
• MLlib - Apache Spark's scalable machine learning library.
• Modin - Speed up your Pandas workflows by changing a single line of code.
• Nebullvm - Easy-to-use library to boost AI inference.
• Nos - Open-source module for running AI workloads on Kubernetes in an optimized way.
• Petastorm - Enables single machine or distributed training and evaluation of deep learning models.
• Rapids - Gives the ability to execute end-to-end data science and analytics pipelines entirely on GPUs.
• Ray - Fast and simple framework for building and running distributed applications.
• Singa - Apache top level project, focusing on distributed training of DL and ML models.
• Tpot - Automated ML tool that optimizes machine learning pipelines using genetic programming.

Tools related to machine learning simplification and standardization.

• Chassis - Turns models into ML-friendly containers that run just about anywhere.
• Hermione - Help Data Scientists on setting up more organized codes, in a quicker and simpler way.
• Hydra - A framework for elegantly configuring complex applications.
• Koalas - Pandas API on Apache Spark. Makes data scientists more productive when interacting with big data.
• Ludwig - Allows users to train and test deep learning models without the need to write code.
• MLNotify - No need to keep checking your training, just one import line and you'll know the second it's done.
• PyCaret - Open source, low-code machine learning library in Python.
• Sagify - A CLI utility to train and deploy ML/DL models on AWS SageMaker.
• Soopervisor - Export ML projects to Kubernetes (Argo workflows), Airflow, AWS Batch, and SLURM.
• Soorgeon - Convert monolithic Jupyter notebooks into maintainable pipelines.
• TrainGenerator - A web app to generate template code for machine learning.
• Turi Create - Simplifies the development of custom machine learning models.

Tools for performing visual analysis and debugging of ML/DL models.

• Aporia - Observability with customized monitoring and explainability for ML models.
• Arize - A free end-to-end ML observability and model monitoring platform.
• CometLLM - Track, visualize, and evaluate your LLM prompts and chains in one easy-to-use UI.
• Evidently - Interactive reports to analyze ML models during validation or production monitoring.
• Fiddler - Monitor, explain, and analyze your AI in production.
• Manifold - A model-agnostic visual debugging tool for machine learning.
• NannyML - Algorithm capable of fully capturing the impact of data drift on performance.
• Netron - Visualizer for neural network, deep learning, and machine learning models.
• Phoenix - MLOps in a Notebook for troubleshooting and fine-tuning generative LLM, CV, and tabular models.
• Superwise - Fully automated, enterprise-grade model observability in a self-service SaaS platform.
• Whylogs - The open source standard for data logging. Enables ML monitoring and observability.
• Yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection.

Tools and frameworks to create workflows or pipelines in the machine learning context.

• Argo - Open source container-native workflow engine for orchestrating parallel jobs on Kubernetes.
• Automate Studio - Rapidly build & deploy AI-powered workflows.
• Couler - Unified interface for constructing and managing workflows on different workflow engines.
• dstack - An open-core tool to automate data and training workflows.
• Flyte - Easy to create concurrent, scalable, and maintainable workflows for machine learning.
• Hamilton - A scalable general purpose micro-framework for defining dataflows.
• Kale - Aims at simplifying the Data Science experience of deploying Kubeflow Pipelines workflows.
• Kedro - Library that implements software engineering best-practice for data and ML pipelines.
• Luigi - Python module that helps you build complex pipelines of batch jobs.
• Metaflow - Human-friendly lib that helps scientists and engineers build and manage data science projects.
• MLRun - Generic mechanism for data scientists to build, run, and monitor ML tasks and pipelines.
• Orchest - Visual pipeline editor and workflow orchestrator with an easy to use UI and based on Kubernetes.
• Ploomber - Write maintainable, production-ready pipelines. Develop locally, deploy to the cloud.
• Prefect - A workflow management system, designed for modern infrastructure.
• VDP - An open-source tool to seamlessly integrate AI for unstructured data into the modern data stack.
• ZenML - An extensible open-source MLOps framework to create reproducible pipelines.

Where to discover new tools and discuss about existing ones.

• A Tour of End-to-End Machine Learning Platforms (Databaseline)
• Continuous Delivery for Machine Learning (Martin Fowler)
• Delivering on the Vision of MLOps: A maturity-based approach (GigaOm)
• Machine Learning Operations (MLOps): Overview, Definition, and Architecture (arXiv)
• MLOps: Continuous delivery and automation pipelines in machine learning (Google)
• MLOps: Machine Learning as an Engineering Discipline (Medium)
• Rules of Machine Learning: Best Practices for ML Engineering (Google)
• The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction (Google)
• What Is MLOps? (NVIDIA)

• Beginning MLOps with MLFlow (Apress)
• Building Machine Learning Pipelines (O'Reilly)
• Building Machine Learning Powered Applications (O'Reilly)
• Deep Learning in Production (AI Summer)
• Designing Machine Learning Systems (O'Reilly)
• Engineering MLOps (Packt)
• Implementing MLOps in the Enterprise (O'Reilly)
• Introducing MLOps (O'Reilly)
• Kubeflow for Machine Learning (O'Reilly)
• Kubeflow Operations Guide (O'Reilly)
• Machine Learning Design Patterns (O'Reilly)
• Machine Learning Engineering in Action (Manning)
• ML Ops: Operationalizing Data Science (O'Reilly)
• MLOps Engineering at Scale (Manning)
• MLOps Lifecycle Toolkit (Apress)
• Practical Deep Learning at Scale with MLflow (Packt)
• Practical MLOps (O'Reilly)
• Production-Ready Applied Deep Learning (Packt)
• Reliable Machine Learning (O'Reilly)
• The Machine Learning Solutions Architect Handbook (Packt)

• apply() - The ML data engineering conference
• MLOps Conference - Keynotes and Panels
• MLOps World: Machine Learning in Production Conference
• NormConf - The Normcore Tech Conference
• Stanford MLSys Seminar Series

• Applied ML
• Awesome AutoML Papers
• Awesome AutoML
• Awesome Data Science
• Awesome DataOps
• Awesome Deep Learning
• Awesome Game Datasets (includes AI content)
• Awesome Machine Learning
• Awesome MLOps
• Awesome Production Machine Learning
• Awesome Python
• Deep Learning in Production

• How AI Built This
• Kubernetes Podcast from Google
• Machine Learning – Software Engineering Daily
• MLOps.community
• Pipeline Conversation
• Practical AI: Machine Learning, Data Science
• This Week in Machine Learning & AI
• True ML Talks

• Kubeflow Workspace
• MLOps Community Wokspace

• Feature Stores for ML
• Made with ML
• ML-Ops
• MLOps Community
• MLOps Guide
• MLOps Now

All contributions are welcome! Please take a look at the contribution guidelines first.