In this whiteboard design session, you will work with a group to design and implement a solution that combines 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, in batch and real-time inferencing scenarios, 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.
Who should attend
This workshop is intended for Cloud Architects and IT professionals who have architectural expertise of infrastructure and solutions design in cloud technologies and want to learn more about Azure and Azure services as described in the ‘About this Course’ and ‘At Course Completion’ areas. Those attending this workshop should also be experienced in other non-Microsoft cloud technologies, meet the course prerequisites, and want to cross-train on Azure.
Delegates will learn how to
The capabilities and implementation solutions of Azure Machine Learning and Azure Databricks.
Module 1: Whiteboard Design Session - Machine Learning
- Review the customer case study
- Design a proof of concept solution
- Present the solution
Module 2: Hands-on lab - Machine Learning
- Creating a forecast model using automated machine learning
- Understanding the automated ML generated forecast model using model explainability
- Creating a deep learning model (RNN) for time series data and registering the model
- Using a forecast model for scoring of streaming telemetry
- Creating a deep learning text classification model