Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

awesome-mlops

:sunglasses: A curated list of awesome MLOps tools
https://github.com/kelvins/awesome-mlops

Last synced: 5 days ago
JSON representation

AutoML
- AutoGluon - Automated machine learning for image, text, tabular, time-series, and multi-modal data.
- H2O AutoML - Automates ML workflow, which includes automatic training and tuning of models.
- 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.
- 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.
- AutoGluon - Automated machine learning for image, text, tabular, time-series, and multi-modal data.
Cron Job Monitoring
- Cronitor - Monitor any cron job or scheduled task.
- HealthchecksIO - Simple and effective cron job monitoring.
Data Catalog
- 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.
- OpenMetadata - A Single place to discover, collaborate and get your data right.
- CKAN - Open-source DMS (data management system) for powering data hubs and data portals.
- 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.
Data Exploration
- Apache Zeppelin - Enables data-driven, interactive data analytics and collaborative documents.
- 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.
- Pandas Profiling - Create HTML profiling reports from pandas DataFrame objects.
- Polynote - The polyglot notebook with first-class Scala support.
- BambooLib - An intuitive GUI for Pandas DataFrames.
- DataPrep - Collect, clean and visualize your data in Python.
- Jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts.
- Pandas Profiling - Create HTML profiling reports from pandas DataFrame objects.
Articles
- The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
- What Is MLOps?
- A Tour of End-to-End Machine Learning Platforms
- Continuous Delivery for Machine Learning
- Delivering on the Vision of MLOps: A maturity-based approach
- Machine Learning Operations (MLOps): Overview, Definition, and Architecture
- MLOps: Continuous delivery and automation pipelines in machine learning
- MLOps: Machine Learning as an Engineering Discipline
- Rules of Machine Learning: Best Practices for ML Engineering
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps Roadmap: A Complete MLOps Career Guide
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
- MLOps: Machine Learning as an Engineering Discipline
Books
- Beginning MLOps with MLFlow
- Building Machine Learning Pipelines
- Building Machine Learning Powered Applications
- Deep Learning in Production
- Designing Machine Learning Systems
- Engineering MLOps
- Implementing MLOps in the Enterprise
- Introducing MLOps
- Kubeflow for Machine Learning
- Kubeflow Operations Guide
- Machine Learning Design Patterns
- Machine Learning Engineering in Action
- ML Ops: Operationalizing Data Science
- MLOps Engineering at Scale
- Practical Deep Learning at Scale with MLflow
- Practical MLOps
- Production-Ready Applied Deep Learning
- Reliable Machine Learning
- The Machine Learning Solutions Architect Handbook
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
- MLOps Lifecycle Toolkit
Events
- 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
- apply() - The ML data engineering conference
Other Lists
- Awesome Data Science
- Awesome Deep Learning
- Awesome Game Datasets
- Awesome Machine Learning
- Awesome Python
Data Management
- Arrikto - Dead simple, ultra fast storage for the hybrid Kubernetes world.
- 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.
- Pinecone - Managed and distributed vector similarity search used with a lightweight SDK.
- 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.
- 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.
- Marquez - Collect, aggregate, and visualize a data ecosystem's metadata.
- Qdrant - An open source vector similarity search engine with extended filtering support.
- Quilt - A self-organizing data hub with S3 support.
- Hub - A dataset format for creating, storing, and collaborating on AI datasets of any size.
Podcasts
- How AI Built This
- Kubernetes Podcast from Google
- Pipeline Conversation
- Practical AI: Machine Learning, Data Science
- This Week in Machine Learning & AI
- True ML Talks
Slack
- Kubeflow Workspace
- MLOps Community Wokspace
Websites
- Feature Stores for ML
- Made with ML
- ML-Ops
- MLOps Community
- MLOps Guide
- MLOps Now
Data Processing
- Airflow - Platform to programmatically author, schedule, and monitor workflows.
- Hadoop - Framework that allows for the distributed processing of large data sets across clusters.
- Spark - Unified analytics engine for large-scale data processing.
- Azkaban - Batch workflow job scheduler created at LinkedIn to run Hadoop jobs.
- Dagster - A data orchestrator for machine learning, analytics, and ETL.
- OpenRefine - Power tool for working with messy data and improving it.
Data Validation
- 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.
- 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.
- TFDV - An library for exploring and validating machine learning data.
Data Visualization
- Count - SQL/drag-and-drop querying and visualisation tool based on notebooks.
- Data Studio - Reporting solution for power users who want to go beyond the data and dashboards of GA.
- Grafana - Multi-platform open source analytics and interactive visualization web application.
- 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.
- Dash - Analytical Web Apps for Python, R, Julia, and Jupyter.
- Facets - Visualizations for understanding and analyzing machine learning datasets.
- Lux - Fast and easy data exploration by automating the visualization and data analysis process.
- SolidUI - AI-generated visualization prototyping and editing platform, support 2D and 3D models.
Drift Detection
- Alibi Detect - An open source Python library focused on outlier, adversarial and 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.
Feature Store
- Feast - End-to-end open source feature store for machine learning.
- Tecton - A fully-managed feature platform built to orchestrate the complete lifecycle of features.
- 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.
- Featureform - A Virtual Feature Store. Turn your existing data infrastructure into a feature store.
Hyperparameter Tuning
- Optuna - Open source hyperparameter optimization framework to automate hyperparameter search.
- Tune - Python library for experiment execution and hyperparameter tuning at any scale.
- Hyperas - A very simple wrapper for convenient hyperparameter optimization.
- Advisor - Open-source implementation of Google Vizier for hyper parameters tuning.
- 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.
- Scikit Optimize - Simple and efficient library to minimize expensive and noisy black-box functions.
- Talos - Hyperparameter Optimization for TensorFlow, Keras and PyTorch.
Knowledge Sharing
- 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.
- Knowledge Repo - Knowledge sharing platform for data scientists and other technical professions.
Machine Learning Platform
- 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.
- 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.
- Modzy - Deploy, connect, run, and monitor machine learning (ML) models in the enterprise and at the edge.
- 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.
- 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.
- envd - Machine learning development environment for data science and AI/ML engineering teams.
- 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.
- SigOpt - A platform that makes it easy to track runs, visualize training, and scale hyperparameter tuning.
- 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.
- Allegro AI - Transform ML/DL research into products. Faster.
Model Interpretability
- SHAP - A game theoretic approach to explain the output of any machine learning model.
- SHAP - A game theoretic approach to explain the output of any machine learning model.
- 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.
Model Lifecycle
- Aim - A super-easy way to record, search and compare 1000s of ML training runs.
- 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.
- Neptune AI - The most lightweight experiment management tool that fits any workflow.
- Weights and Biases - A tool for visualizing and tracking your machine learning experiments.
- Aeromancy - A framework for performing reproducible AI and ML for Weights and Biases.
- 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.
- Keepsake - Version control for machine learning with support to Amazon S3 and Google Cloud Storage.
- MLflow - Open source platform for the machine learning lifecycle.
- 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.
- Comet - Track your datasets, code changes, experimentation history, and models.
Model Serving
- 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.
- BudgetML - Deploy a ML inference service on a budget in less than 10 lines of code.
- Cortex - Machine learning model serving infrastructure.
- KFServing - Kubernetes custom resource definition for serving ML models on arbitrary frameworks.
- Quix - Serverless platform for processing data streams in real-time with machine learning models.
- Seldon - Take your ML projects from POC to production with maximum efficiency and minimal risk.
- TensorFlow Serving - Flexible, high-performance serving system for ML models, designed for production.
- 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.
- Gradio - Create customizable UI components around your models.
- GraphPipe - Machine learning model deployment made simple.
- Hydrosphere - Platform for deploying your Machine Learning to production.
- 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.
- Rune - Provides containers to encapsulate and deploy EdgeML pipelines and applications.
- Streamlit - Lets you create apps for your ML projects with deceptively simple Python scripts.
- 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.
- Geniusrise - Host inference APIs, bulk inference and fine tune text, vision, audio and multi-modal models.
- KFServing - Kubernetes custom resource definition for serving ML models on arbitrary frameworks.
- Banana - Host your ML inference code on serverless GPUs and integrate it into your app with one line of code.
- Wallaroo.AI - A platform for deploying, serving, and optimizing ML models in both cloud and edge environments.
Optimization Tools
- 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.
- Mahout - Distributed linear algebra framework and mathematically expressive Scala DSL.
- MLlib - Apache Spark's scalable machine learning library.
- Rapids - Gives the ability to execute end-to-end data science and analytics pipelines entirely on GPUs.
- Singa - Apache top level project, focusing on distributed training of DL and ML models.
- Accelerate - A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.
- 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.
- Modin - Speed up your Pandas workflows by changing a single line of code.
- 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.
- Ray - Fast and simple framework for building and running distributed applications.
- Nebullvm - Easy-to-use library to boost AI inference.
- Dask - Provides advanced parallelism for analytics, enabling performance at scale for the tools you love.
Simplification Tools
- Chassis - Turns models into ML-friendly containers that run just about anywhere.
- Ludwig - Allows users to train and test deep learning models without the need to write code.
- PyCaret - Open source, low-code machine learning library in Python.
Visual Analysis and Debugging
- Aporia - Observability with customized monitoring and explainability for ML models.
- Arize - A free end-to-end ML observability and model monitoring platform.
- Fiddler - Monitor, explain, and analyze your AI in production.
- 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.
Workflow Tools
- Argo - Open source container-native workflow engine for orchestrating parallel jobs on Kubernetes.
- Automate Studio - Rapidly build & deploy AI-powered workflows.
- Flyte - Easy to create concurrent, scalable, and maintainable workflows for machine learning.
- Kedro - Library that implements software engineering best-practice for data and ML pipelines.
- Metaflow - Human-friendly lib that helps scientists and engineers build and manage data science projects.
- Prefect - A workflow management system, designed for modern infrastructure.
- ZenML - An extensible open-source MLOps framework to create reproducible pipelines.
- Argo - Open source container-native workflow engine for orchestrating parallel jobs on Kubernetes.
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
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.
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.
Model Fairness and Privacy
- 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 Testing & Validation
- 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.

Programming Languages

Python 66 Jupyter Notebook 14 Go 8 Java 7 C++ 3 Rust 2 Scala 2 JavaScript 2 Mustache 1 Makefile 1

Ecosyste.ms: Awesome

awesome-mlops

AutoML

Cron Job Monitoring

Data Catalog

Data Exploration

Articles

Books

Events

Other Lists

Data Management

Podcasts

Slack

Websites

Data Processing

Data Validation

Data Visualization

Drift Detection

Feature Store

Hyperparameter Tuning

Knowledge Sharing

Machine Learning Platform

Model Interpretability

Model Lifecycle

Model Serving

Optimization Tools

Simplification Tools

Visual Analysis and Debugging

Workflow Tools

CI/CD for Machine Learning

Data Enrichment

Feature Engineering

Model Fairness and Privacy

Model Testing & Validation