# Awesome MLOps [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) A curated list of awesome MLOps tools. Inspired by [awesome-python](https://github.com/vinta/awesome-python). - [Awesome MLOps](#awesome-mlops) - [AutoML](#automl) - [CI/CD for Machine Learning](#cicd-for-machine-learning) - [Cron Job Monitoring](#cron-job-monitoring) - [Data Catalog](#data-catalog) - [Data Enrichment](#data-enrichment) - [Data Exploration](#data-exploration) - [Data Management](#data-management) - [Data Processing](#data-processing) - [Data Validation](#data-validation) - [Data Visualization](#data-visualization) - [Drift Detection](#drift-detection) - [Feature Engineering](#feature-engineering) - [Feature Store](#feature-store) - [Hyperparameter Tuning](#hyperparameter-tuning) - [Knowledge Sharing](#knowledge-sharing) - [Machine Learning Platform](#machine-learning-platform) - [Model Fairness and Privacy](#model-fairness-and-privacy) - [Model Interpretability](#model-interpretability) - [Model Lifecycle](#model-lifecycle) - [Model Serving](#model-serving) - [Model Testing & Validation](#model-testing--validation) - [Optimization Tools](#optimization-tools) - [Simplification Tools](#simplification-tools) - [Visual Analysis and Debugging](#visual-analysis-and-debugging) - [Workflow Tools](#workflow-tools) - [Resources](#resources) - [Articles](#articles) - [Books](#books) - [Events](#events) - [Other Lists](#other-lists) - [Podcasts](#podcasts) - [Slack](#slack) - [Websites](#websites) - [Contributing](#contributing) --- ## AutoML *Tools for performing AutoML.* * [AutoGluon](https://github.com/awslabs/autogluon) - Automated machine learning for image, text, tabular, time-series, and multi-modal data. * [AutoKeras](https://github.com/keras-team/autokeras) - AutoKeras goal is to make machine learning accessible for everyone. * [AutoPyTorch](https://github.com/automl/Auto-PyTorch) - Automatic architecture search and hyperparameter optimization for PyTorch. * [AutoSKLearn](https://github.com/automl/auto-sklearn) - Automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. * [EvalML](https://github.com/alteryx/evalml) - A library that builds, optimizes, and evaluates ML pipelines using domain-specific functions. * [FLAML](https://github.com/microsoft/FLAML) - Finds accurate ML models automatically, efficiently and economically. * [H2O AutoML](https://h2o.ai/platform/h2o-automl) - Automates ML workflow, which includes automatic training and tuning of models. * [MindsDB](https://github.com/mindsdb/mindsdb) - AI layer for databases that allows you to effortlessly develop, train and deploy ML models. * [MLBox](https://github.com/AxeldeRomblay/MLBox) - MLBox is a powerful Automated Machine Learning python library. * [Model Search](https://github.com/google/model_search) - Framework that implements AutoML algorithms for model architecture search at scale. * [NNI](https://github.com/microsoft/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](https://github.com/allegroai/clearml) - Auto-Magical CI/CD to streamline your ML workflow. * [CML](https://github.com/iterative/cml) - Open-source library for implementing CI/CD in machine learning projects. ## Cron Job Monitoring *Tools for monitoring cron jobs (recurring jobs).* * [Cronitor](https://cronitor.io/cron-job-monitoring) - Monitor any cron job or scheduled task. * [HealthchecksIO](https://healthchecks.io/) - Simple and effective cron job monitoring. ## Data Catalog *Tools for data cataloging.* * [Amundsen](https://www.amundsen.io/) - Data discovery and metadata engine for improving the productivity when interacting with data. * [Apache Atlas](https://atlas.apache.org) - Provides open metadata management and governance capabilities to build a data catalog. * [CKAN](https://github.com/ckan/ckan) - Open-source DMS (data management system) for powering data hubs and data portals. * [DataHub](https://github.com/linkedin/datahub) - LinkedIn's generalized metadata search & discovery tool. * [Magda](https://github.com/magda-io/magda) - A federated, open-source data catalog for all your big data and small data. * [Metacat](https://github.com/Netflix/metacat) - Unified metadata exploration API service for Hive, RDS, Teradata, Redshift, S3 and Cassandra. * [OpenMetadata](https://open-metadata.org/) - A Single place to discover, collaborate and get your data right. ## Data Enrichment *Tools and libraries for data enrichment.* * [Snorkel](https://github.com/snorkel-team/snorkel) - A system for quickly generating training data with weak supervision. * [Upgini](https://github.com/upgini/upgini) - Enriches training datasets with features from public and community shared data sources. ## Data Exploration *Tools for performing data exploration.* * [Apache Zeppelin](https://zeppelin.apache.org/) - Enables data-driven, interactive data analytics and collaborative documents. * [BambooLib](https://github.com/tkrabel/bamboolib) - An intuitive GUI for Pandas DataFrames. * [DataPrep](https://github.com/sfu-db/dataprep) - Collect, clean and visualize your data in Python. * [Google Colab](https://colab.research.google.com) - Hosted Jupyter notebook service that requires no setup to use. * [Jupyter Notebook](https://jupyter.org/) - Web-based notebook environment for interactive computing. * [JupyterLab](https://jupyterlab.readthedocs.io) - The next-generation user interface for Project Jupyter. * [Jupytext](https://github.com/mwouts/jupytext) - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts. * [Pandas Profiling](https://github.com/ydataai/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects. * [Polynote](https://polynote.org/) - The polyglot notebook with first-class Scala support. ## Data Management *Tools for performing data management.* * [Arrikto](https://www.arrikto.com/) - Dead simple, ultra fast storage for the hybrid Kubernetes world. * [BlazingSQL](https://github.com/BlazingDB/blazingsql) - A lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF. * [Delta Lake](https://github.com/delta-io/delta) - Storage layer that brings scalable, ACID transactions to Apache Spark and other engines. * [Dolt](https://github.com/dolthub/dolt) - SQL database that you can fork, clone, branch, merge, push and pull just like a git repository. * [Dud](https://github.com/kevin-hanselman/dud) - A lightweight CLI tool for versioning data alongside source code and building data pipelines. * [DVC](https://dvc.org/) - Management and versioning of datasets and machine learning models. * [Git LFS](https://git-lfs.github.com) - An open source Git extension for versioning large files. * [Hub](https://github.com/activeloopai/Hub) - A dataset format for creating, storing, and collaborating on AI datasets of any size. * [Intake](https://github.com/intake/intake) - A lightweight set of tools for loading and sharing data in data science projects. * [lakeFS](https://github.com/treeverse/lakeFS) - Repeatable, atomic and versioned data lake on top of object storage. * [Marquez](https://github.com/MarquezProject/marquez) - Collect, aggregate, and visualize a data ecosystem's metadata. * [Milvus](https://github.com/milvus-io/milvus/) - An open source embedding vector similarity search engine powered by Faiss, NMSLIB and Annoy. * [Pinecone](https://www.pinecone.io) - Managed and distributed vector similarity search used with a lightweight SDK. * [Qdrant](https://github.com/qdrant/qdrant) - An open source vector similarity search engine with extended filtering support. * [Quilt](https://github.com/quiltdata/quilt) - A self-organizing data hub with S3 support. ## Data Processing *Tools related to data processing and data pipelines.* * [Airflow](https://airflow.apache.org/) - Platform to programmatically author, schedule, and monitor workflows. * [Azkaban](https://github.com/azkaban/azkaban) - Batch workflow job scheduler created at LinkedIn to run Hadoop jobs. * [Dagster](https://github.com/dagster-io/dagster) - A data orchestrator for machine learning, analytics, and ETL. * [Hadoop](https://hadoop.apache.org/) - Framework that allows for the distributed processing of large data sets across clusters. * [OpenRefine](https://github.com/OpenRefine/OpenRefine) - Power tool for working with messy data and improving it. * [Spark](https://spark.apache.org/) - Unified analytics engine for large-scale data processing. ## Data Validation *Tools related to data validation.* * [Cerberus](https://github.com/pyeve/cerberus) - Lightweight, extensible data validation library for Python. * [Cleanlab](https://github.com/cleanlab/cleanlab) - Python library for data-centric AI and machine learning with messy, real-world data and labels. * [Great Expectations](https://greatexpectations.io) - A Python data validation framework that allows to test your data against datasets. * [JSON Schema](https://json-schema.org/) - A vocabulary that allows you to annotate and validate JSON documents. * [TFDV](https://github.com/tensorflow/data-validation) - An library for exploring and validating machine learning data. ## Data Visualization *Tools for data visualization, reports and dashboards.* * [Count](https://count.co) - SQL/drag-and-drop querying and visualisation tool based on notebooks. * [Dash](https://github.com/plotly/dash) - Analytical Web Apps for Python, R, Julia, and Jupyter. * [Data Studio](https://datastudio.google.com) - Reporting solution for power users who want to go beyond the data and dashboards of GA. * [Facets](https://github.com/PAIR-code/facets) - Visualizations for understanding and analyzing machine learning datasets. * [Grafana](https://grafana.com/grafana/) - Multi-platform open source analytics and interactive visualization web application. * [Lux](https://github.com/lux-org/lux) - Fast and easy data exploration by automating the visualization and data analysis process. * [Metabase](https://www.metabase.com/) - The simplest, fastest way to get business intelligence and analytics to everyone. * [Redash](https://redash.io/) - Connect to any data source, easily visualize, dashboard and share your data. * [SolidUI](https://github.com/CloudOrc/SolidUI) - AI-generated visualization prototyping and editing platform, support 2D and 3D models. * [Superset](https://superset.incubator.apache.org/) - Modern, enterprise-ready business intelligence web application. * [Tableau](https://www.tableau.com) - Powerful and fastest growing data visualization tool used in the business intelligence industry. ## Drift Detection *Tools and libraries related to drift detection.* * [Alibi Detect](https://github.com/SeldonIO/alibi-detect) - An open source Python library focused on outlier, adversarial and drift detection. * [Frouros](https://github.com/IFCA/frouros) - An open source Python library for drift detection in machine learning systems. * [TorchDrift](https://github.com/torchdrift/torchdrift/) - A data and concept drift library for PyTorch. ## Feature Engineering *Tools and libraries related to feature engineering.* * [Feature Engine](https://github.com/feature-engine/feature_engine) - Feature engineering package with SKlearn like functionality. * [Featuretools](https://github.com/alteryx/featuretools) - Python library for automated feature engineering. * [TSFresh](https://github.com/blue-yonder/tsfresh) - Python library for automatic extraction of relevant features from time series. ## Feature Store *Feature store tools for data serving.* * [Butterfree](https://github.com/quintoandar/butterfree) - A tool for building feature stores. Transform your raw data into beautiful features. * [ByteHub](https://github.com/bytehub-ai/bytehub) - An easy-to-use feature store. Optimized for time-series data. * [Feast](https://feast.dev/) - End-to-end open source feature store for machine learning. * [Feathr](https://github.com/linkedin/feathr) - An enterprise-grade, high performance feature store. * [Featureform](https://github.com/featureform/featureform) - A Virtual Feature Store. Turn your existing data infrastructure into a feature store. * [Tecton](https://www.tecton.ai/) - A fully-managed feature platform built to orchestrate the complete lifecycle of features. ## Hyperparameter Tuning *Tools and libraries to perform hyperparameter tuning.* * [Advisor](https://github.com/tobegit3hub/advisor) - Open-source implementation of Google Vizier for hyper parameters tuning. * [Hyperas](https://github.com/maxpumperla/hyperas) - A very simple wrapper for convenient hyperparameter optimization. * [Hyperopt](https://github.com/hyperopt/hyperopt) - Distributed Asynchronous Hyperparameter Optimization in Python. * [Katib](https://github.com/kubeflow/katib) - Kubernetes-based system for hyperparameter tuning and neural architecture search. * [KerasTuner](https://github.com/keras-team/keras-tuner) - Easy-to-use, scalable hyperparameter optimization framework. * [Optuna](https://optuna.org/) - Open source hyperparameter optimization framework to automate hyperparameter search. * [Scikit Optimize](https://github.com/scikit-optimize/scikit-optimize) - Simple and efficient library to minimize expensive and noisy black-box functions. * [Talos](https://github.com/autonomio/talos) - Hyperparameter Optimization for TensorFlow, Keras and PyTorch. * [Tune](https://docs.ray.io/en/latest/tune.html) - Python library for experiment execution and hyperparameter tuning at any scale. ## Knowledge Sharing *Tools for sharing knowledge to the entire team/company.* * [Knowledge Repo](https://github.com/airbnb/knowledge-repo) - Knowledge sharing platform for data scientists and other technical professions. * [Kyso](https://kyso.io/) - One place for data insights so your entire team can learn from your data. ## Machine Learning Platform *Complete machine learning platform solutions.* * [aiWARE](https://www.veritone.com/aiware/aiware-os/) - aiWARE helps MLOps teams evaluate, deploy, integrate, scale & monitor ML models. * [Algorithmia](https://algorithmia.com/) - Securely govern your machine learning operations with a healthy ML lifecycle. * [Allegro AI](https://allegro.ai/) - Transform ML/DL research into products. Faster. * [Bodywork](https://bodywork.readthedocs.io/en/latest/) - Deploys machine learning projects developed in Python, to Kubernetes. * [CNVRG](https://cnvrg.io/) - An end-to-end machine learning platform to build and deploy AI models at scale. * [DAGsHub](https://dagshub.com/) - A platform built on open source tools for data, model and pipeline management. * [Dataiku](https://www.dataiku.com/) - Platform democratizing access to data and enabling enterprises to build their own path to AI. * [DataRobot](https://www.datarobot.com/) - AI platform that democratizes data science and automates the end-to-end ML at scale. * [Domino](https://www.dominodatalab.com/) - One place for your data science tools, apps, results, models, and knowledge. * [Edge Impulse](https://edgeimpulse.com/) - Platform for creating, optimizing, and deploying AI/ML algorithms for edge devices. * [envd](https://github.com/tensorchord/envd) - Machine learning development environment for data science and AI/ML engineering teams. * [FedML](https://fedml.ai/) - Simplifies the workflow of federated learning anywhere at any scale. * [Gradient](https://gradient.paperspace.com/) - Multicloud CI/CD and MLOps platform for machine learning teams. * [H2O](https://www.h2o.ai/) - Open source leader in AI with a mission to democratize AI for everyone. * [Hopsworks](https://www.hopsworks.ai/) - Open-source platform for developing and operating machine learning models at scale. * [Iguazio](https://www.iguazio.com/) - Data science platform that automates MLOps with end-to-end machine learning pipelines. * [Katonic](https://katonic.ai/) - Automate your cycle of intelligence with Katonic MLOps Platform. * [Knime](https://www.knime.com/) - Create and productionize data science using one easy and intuitive environment. * [Kubeflow](https://www.kubeflow.org/) - Making deployments of ML workflows on Kubernetes simple, portable and scalable. * [LynxKite](https://lynxkite.com/) - A complete graph data science platform for very large graphs and other datasets. * [ML Workspace](https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE specialized for machine learning and data science. * [MLReef](https://github.com/MLReef/mlreef) - Open source MLOps platform that helps you collaborate, reproduce and share your ML work. * [Modzy](https://www.modzy.com/) - Deploy, connect, run, and monitor machine learning (ML) models in the enterprise and at the edge. * [Neu.ro](https://neu.ro) - MLOps platform that integrates open-source and proprietary tools into client-oriented systems. * [Omnimizer](https://www.omniml.ai) - Simplifies and accelerates MLOps by bridging the gap between ML models and edge hardware. * [Pachyderm](https://www.pachyderm.com/) - Combines data lineage with end-to-end pipelines on Kubernetes, engineered for the enterprise. * [Polyaxon](https://www.github.com/polyaxon/polyaxon/) - A platform for reproducible and scalable machine learning and deep learning on kubernetes. * [Sagemaker](https://aws.amazon.com/sagemaker/) - Fully managed service that provides the ability to build, train, and deploy ML models quickly. * [SAS Viya](https://www.sas.com/en_us/software/viya.html) - Cloud native AI, analytic and data management platform that supports the analytics life cycle. * [Sematic](https://sematic.dev) - An open-source end-to-end pipelining tool to go from laptop prototype to cloud in no time. * [SigOpt](https://sigopt.com/) - A platform that makes it easy to track runs, visualize training, and scale hyperparameter tuning. * [TrueFoundry](https://www.truefoundry.com) - A Cloud-native MLOps Platform over Kubernetes to simplify training and serving of ML Models. * [Valohai](https://valohai.com/) - 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](https://github.com/Trusted-AI/AIF360) - A comprehensive set of fairness metrics for datasets and machine learning models. * [Fairlearn](https://github.com/fairlearn/fairlearn) - A Python package to assess and improve fairness of machine learning models. * [Opacus](https://github.com/pytorch/opacus) - A library that enables training PyTorch models with differential privacy. * [TensorFlow Privacy](https://github.com/tensorflow/privacy) - Library for training machine learning models with privacy for training data. ## Model Interpretability *Tools for performing model interpretability/explainability.* * [Alibi](https://github.com/SeldonIO/alibi) - Open-source Python library enabling ML model inspection and interpretation. * [Captum](https://github.com/pytorch/captum) - Model interpretability and understanding library for PyTorch. * [ELI5](https://github.com/eli5-org/eli5) - Python package which helps to debug machine learning classifiers and explain their predictions. * [InterpretML](https://github.com/interpretml/interpret) - A toolkit to help understand models and enable responsible machine learning. * [LIME](https://github.com/marcotcr/lime) - Explaining the predictions of any machine learning classifier. * [Lucid](https://github.com/tensorflow/lucid) - Collection of infrastructure and tools for research in neural network interpretability. * [SAGE](https://github.com/iancovert/sage) - For calculating global feature importance using Shapley values. * [SHAP](https://github.com/slundberg/shap) - A game theoretic approach to explain the output of any machine learning model. ## Model Lifecycle *Tools for managing model lifecycle (tracking experiments, parameters and metrics).* * [Aim](https://github.com/aimhubio/aim) - A super-easy way to record, search and compare 1000s of ML training runs. * [Cascade](https://github.com/Oxid15/cascade) - Library of ML-Engineering tools for rapid prototyping and experiment management. * [Comet](https://github.com/comet-ml) - Track your datasets, code changes, experimentation history, and models. * [Guild AI](https://guild.ai/) - Open source experiment tracking, pipeline automation, and hyperparameter tuning. * [Keepsake](https://github.com/replicate/keepsake) - Version control for machine learning with support to Amazon S3 and Google Cloud Storage. * [Losswise](https://losswise.com) - Makes it easy to track the progress of a machine learning project. * [Mlflow](https://mlflow.org/) - Open source platform for the machine learning lifecycle. * [ModelDB](https://github.com/VertaAI/modeldb/) - Open source ML model versioning, metadata, and experiment management. * [Neptune AI](https://neptune.ai/) - The most lightweight experiment management tool that fits any workflow. * [Sacred](https://github.com/IDSIA/sacred) - A tool to help you configure, organize, log and reproduce experiments. * [Weights and Biases](https://github.com/wandb/client) - A tool for visualizing and tracking your machine learning experiments. ## Model Serving *Tools for serving models in production.* * [Banana](https://banana.dev) - Host your ML inference code on serverless GPUs and integrate it into your app with one line of code. * [Beam](https://beam.cloud) - Develop on serverless GPUs, deploy highly performant APIs, and rapidly prototype ML models. * [BentoML](https://github.com/bentoml/BentoML) - Open-source platform for high-performance ML model serving. * [BudgetML](https://github.com/ebhy/budgetml) - Deploy a ML inference service on a budget in less than 10 lines of code. * [Cog](https://github.com/replicate/cog) - Open-source tool that lets you package ML models in a standard, production-ready container. * [Cortex](https://www.cortex.dev/) - Machine learning model serving infrastructure. * [Geniusrise](https://docs.geniusrise.ai) - Host inference APIs, bulk inference and fine tune text, vision, audio and multi-modal models. * [Gradio](https://github.com/gradio-app/gradio) - Create customizable UI components around your models. * [GraphPipe](https://oracle.github.io/graphpipe) - Machine learning model deployment made simple. * [Hydrosphere](https://github.com/Hydrospheredata/hydro-serving) - Platform for deploying your Machine Learning to production. * [KFServing](https://github.com/kubeflow/kfserving) - Kubernetes custom resource definition for serving ML models on arbitrary frameworks. * [LocalAI](https://github.com/mudler/LocalAI) - Drop-in replacement REST API that’s compatible with OpenAI API specifications for inferencing. * [Merlin](https://github.com/gojek/merlin) - A platform for deploying and serving machine learning models. * [MLEM](https://github.com/iterative/mlem) - Version and deploy your ML models following GitOps principles. * [Opyrator](https://github.com/ml-tooling/opyrator) - Turns your ML code into microservices with web API, interactive GUI, and more. * [PredictionIO](https://github.com/apache/predictionio) - Event collection, deployment of algorithms, evaluation, querying predictive results via APIs. * [Quix](https://quix.io) - Serverless platform for processing data streams in real-time with machine learning models. * [Rune](https://github.com/hotg-ai/rune) - Provides containers to encapsulate and deploy EdgeML pipelines and applications. * [Seldon](https://www.seldon.io/) - Take your ML projects from POC to production with maximum efficiency and minimal risk. * [Streamlit](https://github.com/streamlit/streamlit) - Lets you create apps for your ML projects with deceptively simple Python scripts. * [TensorFlow Serving](https://www.tensorflow.org/tfx/guide/serving) - Flexible, high-performance serving system for ML models, designed for production. * [TorchServe](https://github.com/pytorch/serve) - A flexible and easy to use tool for serving PyTorch models. * [Triton Inference Server](https://github.com/triton-inference-server/server) - Provides an optimized cloud and edge inferencing solution. * [Vespa](https://github.com/vespa-engine/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](https://github.com/deepchecks/deepchecks) - Open-source package for validating ML models & data, with various checks and suites. * [Starwhale](https://github.com/star-whale/starwhale) - An MLOps/LLMOps platform for model building, evaluation, and fine-tuning. * [Trubrics](https://github.com/trubrics/trubrics-sdk) - Validate machine learning with data science and domain expert feedback. ## Optimization Tools *Optimization tools related to model scalability in production.* * [Accelerate](https://github.com/huggingface/accelerate) - A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision. * [Dask](https://dask.org/) - Provides advanced parallelism for analytics, enabling performance at scale for the tools you love. * [DeepSpeed](https://github.com/microsoft/DeepSpeed) - Deep learning optimization library that makes distributed training easy, efficient, and effective. * [Fiber](https://uber.github.io/fiber/) - Python distributed computing library for modern computer clusters. * [Horovod](https://github.com/horovod/horovod) - Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. * [Mahout](https://mahout.apache.org/) - Distributed linear algebra framework and mathematically expressive Scala DSL. * [MLlib](https://spark.apache.org/mllib/) - Apache Spark's scalable machine learning library. * [Modin](https://github.com/modin-project/modin) - Speed up your Pandas workflows by changing a single line of code. * [Nebullvm](https://github.com/nebuly-ai/nebullvm) - Easy-to-use library to boost AI inference. * [Nos](https://github.com/nebuly-ai/nos) - Open-source module for running AI workloads on Kubernetes in an optimized way. * [Petastorm](https://github.com/uber/petastorm) - Enables single machine or distributed training and evaluation of deep learning models. * [Rapids](https://rapids.ai/index.html) - Gives the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. * [Ray](https://github.com/ray-project/ray) - Fast and simple framework for building and running distributed applications. * [Singa](http://singa.apache.org/en/index.html) - Apache top level project, focusing on distributed training of DL and ML models. * [Tpot](https://github.com/EpistasisLab/tpot) - Automated ML tool that optimizes machine learning pipelines using genetic programming. ## Simplification Tools *Tools related to machine learning simplification and standardization.* * [Chassis](https://chassisml.io) - Turns models into ML-friendly containers that run just about anywhere. * [Hermione](https://github.com/a3data/hermione) - Help Data Scientists on setting up more organized codes, in a quicker and simpler way. * [Hydra](https://github.com/facebookresearch/hydra) - A framework for elegantly configuring complex applications. * [Koalas](https://github.com/databricks/koalas) - Pandas API on Apache Spark. Makes data scientists more productive when interacting with big data. * [Ludwig](https://github.com/uber/ludwig) - Allows users to train and test deep learning models without the need to write code. * [MLNotify](https://github.com/aporia-ai/mlnotify) - No need to keep checking your training, just one import line and you'll know the second it's done. * [PyCaret](https://pycaret.org/) - Open source, low-code machine learning library in Python. * [Sagify](https://github.com/Kenza-AI/sagify) - A CLI utility to train and deploy ML/DL models on AWS SageMaker. * [Soopervisor](https://github.com/ploomber/soopervisor) - Export ML projects to Kubernetes (Argo workflows), Airflow, AWS Batch, and SLURM. * [Soorgeon](https://github.com/ploomber/soorgeon) - Convert monolithic Jupyter notebooks into maintainable pipelines. * [TrainGenerator](https://github.com/jrieke/traingenerator) - A web app to generate template code for machine learning. * [Turi Create](https://github.com/apple/turicreate) - Simplifies the development of custom machine learning models. ## Visual Analysis and Debugging *Tools for performing visual analysis and debugging of ML/DL models.* * [Aporia](https://www.aporia.com/) - Observability with customized monitoring and explainability for ML models. * [Arize](https://www.arize.com/) - A free end-to-end ML observability and model monitoring platform. * [CometLLM](https://github.com/comet-ml/comet-llm) - Track, visualize, and evaluate your LLM prompts and chains in one easy-to-use UI. * [Evidently](https://github.com/evidentlyai/evidently) - Interactive reports to analyze ML models during validation or production monitoring. * [Fiddler](https://www.fiddler.ai/) - Monitor, explain, and analyze your AI in production. * [Manifold](https://github.com/uber/manifold) - A model-agnostic visual debugging tool for machine learning. * [NannyML](https://github.com/NannyML/nannyml) - Algorithm capable of fully capturing the impact of data drift on performance. * [Netron](https://github.com/lutzroeder/netron) - Visualizer for neural network, deep learning, and machine learning models. * [Phoenix](https://phoenix.arize.com) - MLOps in a Notebook for troubleshooting and fine-tuning generative LLM, CV, and tabular models. * [Superwise](https://www.superwise.ai) - Fully automated, enterprise-grade model observability in a self-service SaaS platform. * [Whylogs](https://github.com/whylabs/whylogs) - The open source standard for data logging. Enables ML monitoring and observability. * [Yellowbrick](https://github.com/DistrictDataLabs/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](https://github.com/argoproj/argo) - Open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. * [Automate Studio](https://www.veritone.com/applications/automate-studio/) - Rapidly build & deploy AI-powered workflows. * [Couler](https://github.com/couler-proj/couler) - Unified interface for constructing and managing workflows on different workflow engines. * [dstack](https://github.com/dstackai/dstack) - An open-core tool to automate data and training workflows. * [Flyte](https://flyte.org/) - Easy to create concurrent, scalable, and maintainable workflows for machine learning. * [Hamilton](https://github.com/dagworks-inc/hamilton) - A scalable general purpose micro-framework for defining dataflows. * [Kale](https://github.com/kubeflow-kale/kale) - Aims at simplifying the Data Science experience of deploying Kubeflow Pipelines workflows. * [Kedro](https://github.com/quantumblacklabs/kedro) - Library that implements software engineering best-practice for data and ML pipelines. * [Luigi](https://github.com/spotify/luigi) - Python module that helps you build complex pipelines of batch jobs. * [Metaflow](https://metaflow.org/) - Human-friendly lib that helps scientists and engineers build and manage data science projects. * [MLRun](https://github.com/mlrun/mlrun) - Generic mechanism for data scientists to build, run, and monitor ML tasks and pipelines. * [Orchest](https://github.com/orchest/orchest/) - Visual pipeline editor and workflow orchestrator with an easy to use UI and based on Kubernetes. * [Ploomber](https://github.com/ploomber/ploomber) - Write maintainable, production-ready pipelines. Develop locally, deploy to the cloud. * [Prefect](https://docs.prefect.io/) - A workflow management system, designed for modern infrastructure. * [VDP](https://github.com/instill-ai/vdp) - An open-source tool to seamlessly integrate AI for unstructured data into the modern data stack. * [ZenML](https://github.com/maiot-io/zenml) - An extensible open-source MLOps framework to create reproducible pipelines. --- # Resources Where to discover new tools and discuss about existing ones. ## Articles * [A Tour of End-to-End Machine Learning Platforms](https://databaseline.tech/a-tour-of-end-to-end-ml-platforms/) (Databaseline) * [Continuous Delivery for Machine Learning](https://martinfowler.com/articles/cd4ml.html) (Martin Fowler) * [Delivering on the Vision of MLOps: A maturity-based approach](https://azure.microsoft.com/mediahandler/files/resourcefiles/gigaom-Delivering-on-the-Vision-of-MLOps/Delivering%20on%20the%20Vision%20of%20MLOps.pdf) (GigaOm) * [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://arxiv.org/abs/2205.02302) (arXiv) * [MLOps: Continuous delivery and automation pipelines in machine learning](https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning) (Google) * [MLOps: Machine Learning as an Engineering Discipline](https://towardsdatascience.com/ml-ops-machine-learning-as-an-engineering-discipline-b86ca4874a3f) (Medium) * [Rules of Machine Learning: Best Practices for ML Engineering](https://developers.google.com/machine-learning/guides/rules-of-ml) (Google) * [The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf) (Google) * [What Is MLOps?](https://blogs.nvidia.com/blog/2020/09/03/what-is-mlops/) (NVIDIA) ## Books * [Beginning MLOps with MLFlow](https://www.amazon.com/Beginning-MLOps-MLFlow-SageMaker-Microsoft/dp/1484265483) (Apress) * [Building Machine Learning Pipelines](https://www.oreilly.com/library/view/building-machine-learning/9781492053187) (O'Reilly) * [Building Machine Learning Powered Applications](https://www.oreilly.com/library/view/building-machine-learning/9781492045106) (O'Reilly) * [Deep Learning in Production](https://www.amazon.com/gp/product/6180033773) (AI Summer) * [Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956) (O'Reilly) * [Engineering MLOps](https://www.packtpub.com/product/engineering-mlops/9781800562882) (Packt) * [Implementing MLOps in the Enterprise](https://www.oreilly.com/library/view/implementing-mlops-in/9781098136574) (O'Reilly) * [Introducing MLOps](https://www.oreilly.com/library/view/introducing-mlops/9781492083283) (O'Reilly) * [Kubeflow for Machine Learning](https://www.oreilly.com/library/view/kubeflow-for-machine/9781492050117) (O'Reilly) * [Kubeflow Operations Guide](https://www.oreilly.com/library/view/kubeflow-operations-guide/9781492053262) (O'Reilly) * [Machine Learning Design Patterns](https://www.oreilly.com/library/view/machine-learning-design/9781098115777) (O'Reilly) * [Machine Learning Engineering in Action](https://www.manning.com/books/machine-learning-engineering-in-action) (Manning) * [ML Ops: Operationalizing Data Science](https://www.oreilly.com/library/view/ml-ops-operationalizing/9781492074663) (O'Reilly) * [MLOps Engineering at Scale](https://www.manning.com/books/mlops-engineering-at-scale) (Manning) * [MLOps Lifecycle Toolkit](https://link.springer.com/book/10.1007/978-1-4842-9642-4) (Apress) * [Practical Deep Learning at Scale with MLflow](https://www.packtpub.com/product/practical-deep-learning-at-scale-with-mlflow/9781803241333) (Packt) * [Practical MLOps](https://www.oreilly.com/library/view/practical-mlops/9781098103002) (O'Reilly) * [Production-Ready Applied Deep Learning](https://www.packtpub.com/product/production-ready-applied-deep-learning/9781803243665) (Packt) * [Reliable Machine Learning](https://www.oreilly.com/library/view/reliable-machine-learning/9781098106218) (O'Reilly) * [The Machine Learning Solutions Architect Handbook](https://www.packtpub.com/product/the-machine-learning-solutions-architect-handbook/9781801072168) (Packt) ## Events * [apply() - The ML data engineering conference](https://www.applyconf.com/) * [MLOps Conference - Keynotes and Panels](https://www.youtube.com/playlist?list=PLH8M0UOY0uy6d_n3vEQe6J_gRBUrISF9m) * [MLOps World: Machine Learning in Production Conference](https://mlopsworld.com/) * [NormConf - The Normcore Tech Conference](https://normconf.com/) * [Stanford MLSys Seminar Series](https://mlsys.stanford.edu/) ## Other Lists * [Applied ML](https://github.com/eugeneyan/applied-ml) * [Awesome AutoML Papers](https://github.com/hibayesian/awesome-automl-papers) * [Awesome AutoML](https://github.com/windmaple/awesome-AutoML) * [Awesome Data Science](https://github.com/academic/awesome-datascience) * [Awesome DataOps](https://github.com/kelvins/awesome-dataops) * [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning) * [Awesome Game Datasets](https://github.com/leomaurodesenv/game-datasets) (includes AI content) * [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) * [Awesome MLOps](https://github.com/visenger/awesome-mlops) * [Awesome Production Machine Learning](https://github.com/EthicalML/awesome-production-machine-learning) * [Awesome Python](https://github.com/vinta/awesome-python) * [Deep Learning in Production](https://github.com/ahkarami/Deep-Learning-in-Production) ## Podcasts * [How AI Built This](https://how-ai-built-this.captivate.fm/) * [Kubernetes Podcast from Google](https://kubernetespodcast.com/) * [Machine Learning – Software Engineering Daily](https://podcasts.google.com/?feed=aHR0cHM6Ly9zb2Z0d2FyZWVuZ2luZWVyaW5nZGFpbHkuY29tL2NhdGVnb3J5L21hY2hpbmUtbGVhcm5pbmcvZmVlZC8) * [MLOps.community](https://podcasts.google.com/?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy8xNzRjYjFiOC9wb2RjYXN0L3Jzcw) * [Pipeline Conversation](https://podcast.zenml.io/) * [Practical AI: Machine Learning, Data Science](https://changelog.com/practicalai) * [This Week in Machine Learning & AI](https://twimlai.com/) * [True ML Talks](https://www.youtube.com/playlist?list=PL4-eEhdXDO5F9Myvh41EeUh7oCgzqFRGk) ## Slack * [Kubeflow Workspace](https://kubeflow.slack.com/#/) * [MLOps Community Wokspace](https://mlops-community.slack.com) ## Websites * [Feature Stores for ML](http://featurestore.org/) * [Made with ML](https://github.com/GokuMohandas/Made-With-ML) * [ML-Ops](https://ml-ops.org/) * [MLOps Community](https://mlops.community/) * [MLOps Guide](https://mlops-guide.github.io/) * [MLOps Now](https://mlopsnow.com) # Contributing All contributions are welcome! Please take a look at the [contribution guidelines](https://github.com/kelvins/awesome-mlops/blob/main/CONTRIBUTING.md) first.