Colleagues, students of the Machine Learning Engineer with Microsoft Azure will gain experience in understanding ML models, protecting people and their data, and controlling the end-to-end ML lifecycle at scale. Develop high-demand and highly marketable skills involving Azure Machine Learning, Azure data services, AI business context, Azure ML platform, Azure ML pipelines, Open neural network exchange, Hyperparameter tuning, Azure ML designer, Model interpretation, Azure ML sdk, Azure ML experiments, Model maintenance, Cloud asset management, Azure ml automated ML, Kubernetes security, Azure ML pipelines, API troubleshooting, REST APIs, Deployment testing, Azure kubernetes service, Swagger, Docker and ApacheBench. Learning modules include: 1) Welcome to Machine Learning Engineer with Microsoft Azure - learn more about the pre-requisites, structure of the program, and getting started, 2) Machine Learning Engineer Program Introduction, the structure of the program and meet your instructors, 3) Using Azure Machine Learning - learn how to configure machine learning pipelines in Azure, identify use cases for Automated Machine Learning, and use the Azure ML SDK to design, create, and manage machine learning pipelines in Azure, 4) Introduction to Azure ML - learn about Machine Learning in the cloud and how workspaces and AzureML studio enable you to be more productive as a data scientist or ML engineer, 5) Datastores and Datasets - learn how to integrate third party datasets and open datasets into our ML pipeline to quickly develop working solutions, 6) Training Models in Azure ML - learn how to manage pipelines and use hyperparameters in experiments, as well as how to automate changes that create huge value in terms of prediction accuracy, 7) The AzureML SDK - programmatically create and manage pipelines. We'll see that this approach makes pipeline creation and management a reproducible process, Automated ML and Hyperparameter Tuning, 8) Optimizing an ML Pipeline in Azure - create and optimize an ML pipeline. You'll do this using both HyperDrive and AutoML, so that you can compare the results, 9) Machine Learning Operations This course covers a lot of the key concepts of operationalizing Machine Learning, from selecting the appropriate targets for deploying models, to enabling Application Insights, identifying problems in logs, and harnessing the power of Azure’s Pipelines. All these concepts are part of core DevOps pillars that will allow you to demonstrate solid skills for shipping machine learning models into production, 10) Operationalizing Machine Learning - work with the Bank Marketing dataset. You will use Azure to configure a cloud-based machine learning production model, deploy it, and consume it, and Capstone - Azure Machine Learning Engineer - this capstone project gives you the opportunity to use the Azure Machine learning knowledge you have obtained from this Nanodegree to solve the problem of your interest.
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