Hidden Differences Between Azure ML Products Revealed

Explore the hidden differences between various Azure Machine Learning (AML) products in this 55-minute conference talk from PASS Data Community Summit. Gain insights into selecting the appropriate tool for integrating with SQL Server or Azure data. Learn about AML Studio, Workspace, Pipeline, and Service, understanding their unique features and use cases. Follow along with demonstrations showcasing how to create an end-to-end AML workflow, including data preparation, model creation, deployment, and integration with SQL Server using tools like Databricks, Python, and Jupyter notebooks. Discover the benefits of AML Workspace for collaborative ML development, scaling, and management. Understand how to effectively monitor ML models in production using AML Pipeline for organized releases and execution metrics generation. By the end of this talk, acquire the knowledge needed to architect appropriate solutions for your specific environment using Azure ML products.

Intro
Ginger Grant
Azure ML Options
Azure ML Services
Azure Workbench
Azure Machine Learning
Machine Learning Workspace
ML Ops
Azure Machine Learning SDK
Azure Pipelines
Open Neural Network Exchange
Deployment
Notebooks
Automated Machine Learning
Creating a workspace
Azure Machine Learning Workspace
Azure Machine Learning Ver
Azure ML Studio
Azure Notebooks
Databricks
Recap