Take a look at the notebook in Github: https://github.com/yaronha/demos/tree/azureml-demo/azureml-demo

AutoMLOps means automating engineering tasks so that your code is automatically ready for production. In this session, Yaron outlined the challenges, describe open-source tools available for Auto-MLOps, and more!

In this session, we will describe the challenges in operationalizing machine & deep learning. We’ll explain the production-first approach to MLOps pipelines - using a modular strategy, where the different components provide a continuous, automated, and far simpler way to move from research and development to scalable production pipelines. Without the need to refactor code, add glue logic, and spend significant efforts on data and ML engineering.

As the MLOps practice matures, there is an accumulation of stories about what works well – and what doesn’t. If you’re building up your enterprise MLOps muscle, instead of trial and error, why not tap into the collective memory of thousands of organizations who have spent the last couple of years building their MLOps practices internally and learn from their experience?