In this workshop, you will gain a better understanding of how to combine Azure Databricks with Azure Machine Learning to build, train and deploy machine learning and deep learning 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 workshop, you will be able to design a solution better understanding the capabilities of leveraging the Azure Machine Learning service and Azure Databricks.

Azure services and related products:
• Azure Databricks
• Azure Machine Learning
• Azure Machine Learning Automated
• Machine Learning
• Azure Storage
• IoT Hub
• PyTorch
• Power BI

Speaker's Bio: Shivam Sharma, Co-founder TechScalable

Shivam is an author, cloud architect, speaker and Co-Founder at TechScalable. Being passionate about ever evolving technology he works on Azure, GCP, Machine Learning & Blockchain. He is also a Microsoft Certified Trainer. He architects’ solutions on Cloud as well on-premises using wide array of platforms/technologies.