# All-in-one Solutions {% embed url="" %} All In One - Infrastructure and Tooling {% endembed %} ## Summary * 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/all-in-one-solutions.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.