Tying everything together – Solving a Machine Learning problem in the Cloud (Part 4 of 4) | AI Show

This is the final, part 4 of a four-part series that breaks up a talk that I gave at the Toronto AI Meetup. Part 1, Part 2 and Part 3 were all about the foundations of machine learning, optimization, models, and even machine learning in the cloud. In this video I show an actual machine learning problem (see the GitHub repo for the code) that does the important job of distinguishing between tacos and burritos (an important problem to be sure). The primary concepts included is MLOps both on the machine learning side as well as the deliver side in Azure Machine Learning and Azure DevOps respectively. [00:28] Tacos vs burritos [04:15] Prep, Train, and export model[13:43] Machine learning pipelines[14:46] MLOpsHope you enjoy the final of the series, Part 4! As always feel free to send any feedback or add any comments below if you have any questions. If you would like to see more of this style of content let me know! The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show Create a Free account (Azure) Deep Learning vs. Machine Learning Get Started with Machine Learning