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Session #19

From AutoML to AutoMLOps: Automated Logging & Tracking of ML

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The requirements for building, deploying and managing AI applications in production mean significant MLOps and engineering efforts, rendering the old training-first paradigm insufficient. The solution? AutoMLOps.
AutoMLOps means automating the many engineering tasks of deploying ML, so that your code is automatically ready for production. Oh, and there are open-source tools out there that enable it! AutoMLOps includes:
  • Automatically converting code to managed microservices and reusable components
  • Auto-tracking experiments, metrics, artifacts, data, models
  • Automatically registering models along with their required metadata and optimal production formats
  • Auto-scaling and automatically optimizing resource usage (such as CPUs / GPUs)
  • Codeless Integration with different dashboards, profilers, CI/CD frameworks, etc.
 
 In this session, we outline the challenges, describe open-source tools available for Auto-MLOps, and finish off with a live demo.