A curated list of awesome machine learning engineering tools.
Inspired by awesome-python.
Tools for monitoring cron jobs (recurring jobs).
- HealthchecksIO - Simple and effective cron job monitoring.
Tools for performing data exploration.
- 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.
Tools related to data processing and data pipelines.
- Airflow - Platform to programmatically author, schedule, and monitor workflows.
- Hadoop - Framework that allows for the distributed processing of large data sets across clusters of computers.
- Spark - Unified analytics engine for large-scale data processing.
Tools for performing data version control.
- DVC - Management and versioning of datasets and machine learning models.
Tools for data visualization, reports and dashboards.
- Data Studio - Reporting solution for power users who want to go beyond the data and dashboards of Google Analytics.
- 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.
Feature store tools for data serving.
- Feast - End-to-end open source feature store for machine learning.
Tools and libraries to perform hyperparameter tuning.
- Katib - Kubernetes-based system for hyperparameter tuning and neural architecture search.
- 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.
- Algorithmia - Securely govern your machine learning operations with a healthy ML lifecycle.
- CNVRG - An end-to-end machine learning platform to build and deploy AI models at scale.
- 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 machine learning at scale.
- Domino - One place for your data science tools, apps, results, models, and knowledge.
- 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.
- Knime - Create and productionize data science using one easy and intuitive environment.
- Kubeflow - Making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable.
- Modzy - AI platform and marketplace offering scalable, secure, and ready-to-deploy AI models.
- 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.
Tools for managing model lifecycle (tracking experiments, parameters and metrics).
- Comet - Track your datasets, code changes, experimentation history, and models.
- Mlflow - Open source platform for the machine learning lifecycle.
- Neptune AI - The most lightweight experiment management tool that fits any workflow.
Tools for serving models in production.
- BentoML - Open-source platform for high-performance ML model serving.
- Cortex - Machine learning model serving infrastructure.
- GraphPipe - Machine learning model deployment made simple.
- KFServing - Kubernetes custom resource definition for serving machine learning (ML) models on arbitrary frameworks.
- PredictionIO - Supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs.
- Seldon - Take your ML projects from POC to production with maximum efficiency and minimal risk.
- TensorFlow Serving - Flexible, high-performance serving system for machine learning models, designed for production.
Optimization tools related to model scalability in production.
- 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.
- Modin - Speed up your Pandas workflows by changing a single line of code.
- Ray - Fast and simple framework for building and running distributed applications.
- Singa - Apache top level project, focusing on distributed training of deep learning and machine learning models.
- Tpot - Automated machine learning tool that optimizes machine learning pipelines using genetic programming.
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.
- Kedro - Library that implements software engineering best-practice for data and ML pipelines.
- Metaflow - Human-friendly library that helps scientists and engineers build and manage real-life data science projects.
- Prefect - A workflow management system, designed for modern infrastructure.
Where to discover new tools and discuss about existing ones.
- Continuous Delivery for Machine Learning (Martin Fowler)
- Continuous delivery for machine learning (ThoughtWorks)
- Kubernetes Podcast from Google
- Machine Learning – Software Engineering Daily
- MLOps.community
- This Week in Machine Learning & AI
All contributions are welcome! Please take a look at the contribution guidelines first.