How to choose between different machine learning platforms?
All In One - Infrastructure and Tooling
- The “All-In-One” machine learning platforms provide a single system for everything: developing models, scaling experiments to many machines, tracking experiments and versioning models, deploying models, and monitoring model performance.
- FBLearner Flow is the workflow management platform at the heart of the Facebook ML engineering ecosystem.
- Michelangelo, Uber’s ML Platform, supports the training and serving of thousands of models in production across the company.
- TensorFlow Extended (TFX) is a Google-production-scale ML platform based on TensorFlow.
- Another option from Google is its Cloud AI Platform, a managed service that enables you to easily build machine learning models, that work on any type of data, of any size.
- Amazon SageMaker is one of the core AI offerings from AWS that helps teams through all stages in the machine learning life cycle.
- Neptune is a product that focuses on managing the experimentation process while remaining lightweight and easy to use by any data science team.
- FloydHub is another managed cloud platform for data scientists.
- Paperspace provides a solution for accessing computing power via the cloud and offers it through an easy-to-use console where everyday consumers can just click a button to log into their upgraded, more powerful remote machine.
- Determined AI is a startup that creates software to handle everything from managing cluster compute resources to automating workflows, thereby putting some of that big-company technology within reach of any organization.
- Domino Data Lab is an integrated end-to-end platform that is language agnostic, having a rich functionality for version control and collaboration; as well as one-click infrastructure scalability, deployment, and publishing.