In this module, you will work with a group to design and implement a solution that combines Azure Databricks with Azure Machine Learning to build, train, and deploy machine learning and deep learning models. You will learn how to prepare data for training and use automated machine learning and model lifecycle management from training to deployment (in batch and real-time inferencing scenarios). You will also learn to build deep learning models for Natural Language Processing (NLP) in text classification and forecasting against time-series data and address the model interpretability problem. Finally, you will learn how to use MLflow for managing experiments run directly on the Azure Databricks cluster and how MLflow can seamlessly log metrics and training artifacts in your Azure Machine Learning workspace. In the process, you will also get to compare data with PyTorch and Keras for deep learning. At the end of this module, you will have a deeper understanding of the capabilities and implementation solutions when leveraging Azure Machine Learning and Azure Databricks.

In this module, you will use Azure Databricks in combination with Azure Machine Learning to build, train and deploy desired models. You will learn how to train a forecasting model against time-series data, without any code, by using automated machine learning, and how to interpret trained machine learning models. You will also learn how to use MLflow for managing experiments run directly on the Azure Databricks cluster and how MLflow can seamlessly log metric and training artifacts in your Azure Machine Learning workspace. You will create a recurrent neural network (RNN) model using PyTorch in Azure Databricks that can be used to forecast against time-series data and train a Natural Language Processing (NLP) text classification model based on Long Short-Term Memory (LSTM) recurrent neural network and Keras. At the end of this module, you will be better able to build solutions leveraging Azure Machine Learning and Azure Databricks.