Enabling Push Button Productization of AI Models

Hear key learnings from building AI platforms for deploying ML models to production at scale. This includes a fully automated continuous delivery process and systematic measures to minimize the cost and effort required to sustain the models in production. The talk includes examples from different business domains and deployment scenarios covering their architecture which is based, in most cases, on streaming, microservice architecture optimized to be easily deployed with Docker containers and Kubernetes. This method offers a good separation of concerns as Data scientists don’t have to care about engineering aspects which are not part of their expertise. A data scientist can just push the model, which is code, while complying to some standards – and the rest will happen automagically. This code will be built, tested, deployed and activated in an AI platform that already has all the integration hooks to the biz domain. In addition, it offers cool manageability aspects that help track and maintain the model in production and reduce its total cost of ownership. This includes features such as applicative monitoring, model health indicators, re-train of models and more.

Key Takeaways:

  1. Discover how to enable continuous delivery and sustainability for AI
  2. Learn what technologies are used to enable this process
  3. Understand the rational behind AI platforms for productizing ML models
  4. See real business examples with a thorough overview of the architecture and related open source technologies

 
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About Moty Fania

Intel Corporation

Moty Fania is a principal engineer and the CTO of the Advanced Analytics Group at Intel, which delivers AI and big data solutions across Intel. Moty has rich experience in ML engineering, analytics, data warehousing, and decision-support solutions. He led the architecture work and development of various AI and big data initiatives such as IoT systems, predictive engines, online inference systems, and more.