From the course: Microsoft Azure Data Scientist Associate (DP-100) Cert Prep

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Conclusion

Conclusion - Azure Tutorial

From the course: Microsoft Azure Data Scientist Associate (DP-100) Cert Prep

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Conclusion

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- [Instructor] All right, we're at the end of this course. Let's go ahead and wrap up what we covered. In Domain 1, 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 datasets. In Domain 2, 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 3, 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 4, 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 datasets, and finally, things like triggering Azure machine learning pipelines from GitHub. All right, you're all set to prepare for the exam. Good luck.

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