Top 10 Open Source MLOps Tools
Using top open source MLOps tools, platforms, and frameworks such as the ones highlighted in this article, you can leverage the scalability and flexibility offered by MLOps in your Machine Learning workflows.
MLOps refers to the combined usage of DevOps and Machine Learning to create robust automation, tracking, pipelining, monitoring, and packaging system for Machine Learning models.
Open source MLOps tools give users the freedom to enjoy the automation and flexibility offered by MLOps without spending a fortune.
Arranged in the order of their number of GitHub stars, below are top 10 open source MLOps tools.
Kubeflow is a full-fledged open source MLOps tool that makes the orchestration and deployment of Machine Learning workflows easier. Kubeflow provides dedicated services and integration for various phases of Machine Learning, including training, pipeline creation, and management of Jupyter notebooks.
It integrates with various frameworks such as Istio and also handles TensorFlow training jobs easily.
Kubeflow has over 10.3k stars and 222 contributors on GitHub, giving it the top spot on this list.
MLFlow is an open source Machine Learning lifecycle management platform that offers various components in experiments tracking, project packaging, model deployment, and registry. MLFlow integrates with various Machine Learning libraries including TensorFlow and Pytorch, to streamline the training, deployment, and management of Machine Learning applications.
Data Version Control (DVC)
DVC is a python written open source tool for Data Science and Machine Learning projects. It takes on a Git-like model to provide management and versioning of datasets and machine learning models. DVC is a simple command-line tool that makes machine learning projects shareable and reproducible.
DVC is a regularly updated tool with over 7.9k stars and 212 contributors on GitHub.
Like DVC, Pachyderm is a version-control tool for Machine Learning and Data Science. In addition to that, it is built on Docker and Kubernetes, which helps it run and deploy Machine Learning projects to any cloud platform. Pachyderm ensures that every data ingested into a Machine Learning model is versioned and retraceable.
Pachyderm is an open source Machine Learning tool written in Golang and has over 5,000 stars on GitHub.
Metaflow is an open source MLOps platform initially developed by Netflix. It is a Python/R-written tool that makes it easy to build and manage enterprise Data Science projects.
Metaflow integrates Python-based Machine Learning, Deep Learning, and Big data libraries to efficiently train, deploy, and manage ML models.
It has over 4k stars and more than 30 contributors making regular updates to the tool on GitHub.
Kedro is a Python-written open source MLOps framework used for creating reproducible and maintainable Data Science code. It implements software engineering practices such as versioning and modularity in Machine Learning projects.
It offers pipeline visualization, project templating, and flexible deployment of Data Science projects.
Kendro has over 3.9k stars on GitHub.
Seldon is an open source MLOps framework designed to streamline Machine Learning workflows with logging, advanced metrics, testing, scaling, and conversion of models into production microservices.
Seldon offers some high-level features that make it easy to containerize ML models, test the usability and security of models and make them fully auditable by integrating with several services.
A large percentage of Seldon was created with Jupyter Notebook, and it has over 2.3k stars on GitHub.
Flyte is another open source MLOps platform used for tracking and maintaining, and automating Kubernetes-native Machine Learning workflows. It ensures that the execution of Machine Learning models is reproducible by tracking changes to the model, versioning it, and containerizing the model alongside its dependencies.
Flyte is written in Python and is designed to support complex ML workflows written in Python, Java, and Scala.
The tool has 1.4k stars and over 38 contributors on GitHub.
ZenML is an extensible open source MLOps framework that integrates ML tools such as Jupyter notebooks to deploy ML models into employment coherently and straightforward. ZenML is used to create reproducible Machine Learning pipelines for producing Machine Learning projects.
The framework is written in Python and it has over 1.1k stars on GitHub.
MLRun is an open source MLOps framework that helps you manage your Machine Learning pipeline from the development phase all through the deployment into production. MLRun introduces tracking, automation, rapid deployment, management, and easy scaling of models into your Machine Learning pipeline.
It is a Python-written framework with over 300 stars on GitHub
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