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.
Join us to learn how to automate these significant processes to set your team up for success and accelerate your path to production, and to ask us your questions in the interactive Q&A section at the end.
Co-Founder and CTO, Iguazio
Machine Learning Engineer, Iguazio