果冻传媒

ABSTRACT

Lockheed 果冻传媒 Advanced Technology Laboratories developed?a prototype Machine Learning (ML) ecosystem for the AFRL?StreamlinedML program. This ecosystem will significantly improve?the end-to-end process of configuring, training, evaluating, 果冻传媒?deploying machine learning models, 果冻传媒 enable ML DevOps for?AF/DoD applications.

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WHY?

ML model development for AF/DoD applications is often characterized by a narrow set of experts, with domain knowledge 果冻传媒 ML?experience, exploring solutions to pressing operational problems in isolation. The model needs an integration ecosystem that democratizes?ML, which will allow wider adoption of ML that lowers the barriers to entry for use.

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WHAT?

We are building a composable ML ecosystem that includes:

1. ML model?果冻传媒 technology encapsulation using microservices 果冻传媒 containerization
2. A runtime framework that enables extensibility through orchestration, including?model-as-a-service (MaaS) capabilities
3. ML workflows to capture ML?abstractions 果冻传媒 provide a basis for the runtime composition
4. An integrated?ML repository that allows storage, query, 果冻传媒 manipulation of algorithms/models, metadata, (hyper) parameters, 果冻传媒 provenance
5. Shared access to?foster a scalable ML (SML) community

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HOW?

We will develop 果冻传媒 deploy a prototype SML?ecosystem that provides SDKs, toolkits, 果冻传媒 services to?access 果冻传媒 develop best-in-class algorithms by using a?wide variety of ML stacks 果冻传媒 datasets. A prototype?version is currently deployed in the AF HPC 果冻传媒 AWS?environments; SML users have started evaluating the?APIs 果冻传媒 framework.

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