Conclusion - Azure Tutorial

- [Instructor] All right, we're at the end of this course. Let's go ahead and wrap up what we covered. In domain one, we covered how to design and prepare a machine learning solution. This included concepts like how to configure compute specifications, how to configure workspaces, and manage data sets. In domain two, we covered how to explore data and train models, including transforming data using Azure Data Explorer, the designer, ML Studio, Notebooks and also how to use the Python SDK. In domain three, we covered how to prepare a model for deployment, including how to use GitHub to Azure feedback loop, how to explore models, how to describe MLflow models. And then finally, in domain four, we covered how to deploy and retrain a model. Includes concepts such as realtime and batch deployment, end-to-end, Databricks MLflow and also Azure open data sets. And finally, things like triggering Azure machine learning pipelines…