👌TOP-5 MLOps frameworks
The MLOps topic is in hype today: ML systems are getting more complex, and their development and support includes not only computational algorithms and other data science, but also the best software developments with the intricacies of deploying in production. At the same time, it is necessary to constantly monitor the drift of the input data and the reaction of ML models to them, with the accuracy of correcting the code with its versioning. Establish such a continuous chain of response to complex MLOps frameworks, the most important of which are possible:
• MLflow is an open source platform for managing the end-to-end machine learning lifecycle. https://www.mlflow.org/
• Kubeflow is an open-source machine learning platform designed to enable using machine learning pipelines to orchestrate complicated workflows running on Kubernetes (e.g. doing data processing then using TensorFlow or PyTorch to train a model, and deploying to TensorFlow Serving or Seldon). Kubeflow is built based on Google’s internal method to deploy TensorFlow models called TensorFlow Extended. https://www.kubeflow.org/
• FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. It fully supports asynchronous programming and can run with Uvicorn and Gunicorn. https://fastapi.tiangolo.com/
• ZenML is an open-source MLOps framework built for ML teams. It provides the abstraction layer to bring Machine Learning Models from research to production as easily as possible. Data scientists don’t need to know the details behind the deployment but gain full control and ownership over the whole pipeline process. ZenML standardizes writing ML pipelines across different MLOps stacks, agnostic of cloud providers, third-party vendors, and underlying infrastructure. https://zenml.io/
• Seldon Core is an open-source framework, makes it easier and faster to deploy our machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment. Seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including Advanced Metrics, Request Logging, Explainers, Outlier Detectors, A/B Tests, Canaries, and more. https://www.seldon.io/solutions/open-source-projects/core