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A selection of the finest MLOps tools to help you construct the ideal machine learning stack.
SHapley Additive exPlanations is a game-theoretic and model-agnostic approach to explain the output generated by any machine learning model.
Neptune metadata store helps research and production teams to organize all metadata generated during the ML lifecycle in a single place.
A Python library that helps visualize and debug ML models using unified API and offers a way to explain black-box models.
A cron job monitoring tool that alerts for job failure, unexpected crashes, and delays. Tracks status, metrics, and logs from every job.
Healthchecks.io is an online service that monitors cron jobs and similar periodic processes by listening to HTTP requests or pings
Flyte is a high-end, open-source tool that helps create scalable, concurrent, and maintainable workflows for machine learning projects.
A distributed deep learning training framework for TensorFlow, PyTorch, Keras, and Apache MXNet to accelerate and optimize training
MLlib is Apache Spark’s highly scalable and easy-to-use ML library that supports ML algorithms, featurization, and utilities.
KFServing or KServe allows serverless inferencing on Kubernetes and provides high abstraction interfaces for common ML frameworks.
BentoML is a flexible, high-performance framework that helps serve, manage and deploy trained ML models to production.
Multi-purpose, web-based notebook that supports data-driven interactive analytics and collaborative documents with Python, SQL, R, and more.
An open-source, container-native workflow engine that helps orchestrate parallel jobs on Kubernetes using DAGs and step-based workflows.
A Python package and task orchestrator that helps build complex pipelines of batch jobs to manage failures, dependencies, and workflows.
An open-source, easy-to-use Python library that helps build and deploy customized web apps for machine learning models in minutes.
Cortex is an open-source, flexible, and scalable tool that helps ML deployments with multifold tasks such as model serving and monitoring.
Feast is an open-source and easy-to-use operational data system to manage and serve machine learning features to models in production.
An open-source and scalable library for parallel computing in Python that offers dynamic task scheduling and ‘Big data’ collections.
A data exploration and visualization platform helping users to visualize data from simple line and pie charts to complex geospatial charts.
An open-source framework to deploy machine learning models and experiments at scale on Kubernetes.
An open-source platform to manage the entire ML lifecycle by supporting experimentation, reproducibility, deployment, and model storage.
A modern and open-source workflow management system that helps orchestrate data stack and integrate best workflow practices.
An open-source and robust task orchestration platform to monitor, schedule, and manage workflows programmatically.
An open-source data version control tool that brings reproducibility, agility, and collaboration into ML workflows.
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