ML and ML-Ops with Azure Machine Learning service, Azure Databricks and Azure DevOps

Duration: 3 Days
Level: Intermediate
Learning Mode : Live Online Instructor-Led Training
In this workshop, you will gain a better understanding of how to combine Azure Databricks with Azure Machine Learning service to build, train and deploy the machine learning and deep learning models. You will learn how to use automated machine learning, model lifecycle management from training to deployment, and construct deep learning models for Natural Language Processing (NLP) in text classification and forecasting against time-series data. Finally, you’ll learn to compare data with PyTorch and Keras for deep learning. On Day 3, you will work in a group to design a process a customer can follow for orchestrating and deploying updates to the application and the deep learning model in a unified way. You will learn how the customer can leverage Deep Learning technologies to scan through their vehicle specification documents to find compliance issues with new regulations. You will standardize the model format to ONNX and observe how this simplifies inference runtime code, enabling pluggability of different models and targeting a broad range of runtime environments and most importantly improves inferencing speed over the native model. You will design a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application and inferencing web service. You will also learn how to monitor the model's performance after it is deployed so the customer can be proactive with performance issues. At the end of this workshop, you will be able to design a solution better understanding the capabilities of leveraging the Azure Machine Learning service and Azure Databricks. You will be better able to design end-to-end solutions that will fully operationalize deep learning models, inclusive of all application components that depend on the model.

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Course Code TF210029 · Section Code 3 Days | On Request