Stepes
Stepes is an open source platform for building and deploying machine learning models. It allows data scientists and developers to track experiments, version models, and monitor production models.
Stepes: Open Source Machine Learning Platform
An open source platform for building and deploying machine learning models, tracking experiments, versioning models, and monitoring production models
What is Stepes?
Stepes is an open source machine learning operations (MLOps) platform for building, deploying, and managing machine learning models. It provides a centralized platform to:
- Quickly build machine learning pipelines with low/no code components
- Track experiments through an ML-aware version control system
- Monitor models in production to detect data drift
- Simplify collaboration between data scientists and engineering teams
Key capabilities and benefits of Stepes include:
- Intuitive graphical interface to build ML pipelines with pre-built components
- MLOps tracking of model versions, experiments, data sets, and metrics
- Collaboration tools for teams to work together on projects
- Model monitoring to detect data drift and model degradation
- Open source platform that can be self-hosted on internal infrastructure
Overall, Stepes simplifies many key parts of the machine learning lifecycle, enabling faster experimentation for data scientists paired with rigor and governance for production deployments. Its versatility and ease-of-use makes it a popular choice for ML teams looking to scale their efforts.
Stepes Features
Features
- Open source platform for building and deploying machine learning models
- Allows tracking experiments, versioning models, and monitoring production models
- Supports popular machine learning frameworks like TensorFlow, PyTorch, Keras
- Integrated with CI/CD tools for continuous training and deployment
- Built-in monitoring and alerting for models in production
- Collaboration features like comments and approvals on models and experiments
Pricing
- Open Source
Pros
Free and open source
Great for experiment tracking and model versioning
Production monitoring helps maintain accuracy of models
Integrations with GitHub, Slack etc. improves workflow
Scalable architecture to train and deploy large models
Cons
Limited model deployment options compared to end-to-end MLOps platforms
Less support and integrations than commercial offerings
Not as user friendly as some commercial alternatives
Requires more technical knowledge to set up and use
Official Links
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