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.