API World + AI DevWorld

Low latency applications - such as cloud gaming, immersive media streaming, Industrial IOT, Video surveillance and data reduction applications such as Video CDN are driving edge computing, more specifically distributed edge computing.  Geo-distributed computing needs geo-distributed analytics.  Bootstrapping collection, big data lakes and AI frameworks for a single site takes weeks and think of amount of time it requires to bring up big data AI framework in multiple edge locations. In this session, we will talk about AI framework, which we developed in Linux Foundation as open source. This framework can be used to many AI applications together, developed in any language and used any ML/DL frameworks. This Microservice based AI frame architecture can be deployed anywhere where Kubernetes is used. In this session, we will also talk about the configuration flexibility to deploy in heterogeneous environments. Deploying and configuration framework in tens of thousands of edge locations require automation. We will also talk about the work done on deployer intent and auto deployment of AI framework components on static edge locations as well as dynamic edges.  If you are edge provider or deploying applications in edges, you will walk away with a scalable and practical AI framework that can be used to deploy your analytics applications.