/awesome-mlops

:sunglasses: A curated list of awesome MLOps tools

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Awesome MLOps Awesome

A curated list of awesome MLOps tools.

Inspired by awesome-python.

AutoML

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.

CI/CD for Machine Learning

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.

Cron Job Monitoring

Tools for monitoring cron jobs (recurring jobs).

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

Data Catalog

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.

Data Enrichment

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.

Data Exploration

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.

Data Management

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.

Data Processing

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.

Data Validation

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.

Data Visualization

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.
  • 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.
  • Superset - Modern, enterprise-ready business intelligence web application.
  • Tableau - Powerful and fastest growing data visualization tool used in the business intelligence industry.

Drift Detection

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.

Feature Engineering

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

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.

Hyperparameter Tuning

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.

Knowledge Sharing

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.

Machine Learning Platform

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 - AI platform and marketplace offering scalable, secure, and ready-to-deploy AI models.
  • 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.

Model Fairness and Privacy

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.

Model Interpretability

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.
  • Skater - Unified framework to enable Model Interpretation for all forms of model.

Model Lifecycle

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.
  • Replicate - Library that uploads files and metadata (like hyperparameters) to S3 or GCS.
  • 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.

Model Serving

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.
  • Cortex - Machine learning model serving infrastructure.
  • 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.
  • 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.

Model Testing & Validation

Tools for testing and validating models.

  • Deepchecks - Open-source package for validating ML models & data, with various checks and suites.
  • Trubrics - Validate machine learning with data science and domain expert feedback.

Optimization Tools

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.

Simplification Tools

Tools related to machine learning simplification and standardization.

  • 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.

Visual Analysis and Debugging

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.
  • 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.

Workflow Tools

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.

Resources

Where to discover new tools and discuss about existing ones.

Articles

Books

Events

Other Lists

Podcasts

Slack

Websites

Contributing

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