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1
Intro
2
Ginger Grant
3
Azure ML Options
4
Azure ML Services
5
Azure Workbench
6
Azure Machine Learning
7
Machine Learning Workspace
8
ML Ops
9
Azure Machine Learning SDK
10
Azure Pipelines
11
Open Neural Network Exchange
12
Deployment
13
Notebooks
14
Automated Machine Learning
15
Creating a workspace
16
Azure Machine Learning Workspace
17
Azure Machine Learning Ver
18
Azure ML Studio
19
Azure Notebooks
20
Databricks
21
Recap
Description:
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.

Hidden Differences Between Azure ML Products Revealed

PASS Data Community Summit
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