Azure ML Studio and SDK – Overview
Navigate Azure ML Studio, notebooks, the Python SDK v2 and CLI v2 with a first end-to-end job.
...local tools show their limits. That's where Azure Machine Learning comes in.
Sign in to read this course
A free account unlocks all 514 courses. 20 are readable without one.
What's inside
19 sections- 1 Table of Contents
- 2 Azure ML Overview
- 3 Azure ML Studio – Visual Interface
- 4 Notebooks in Azure ML Studio
- 5 Azure ML SDK v2 in Python
- 6 Azure ML CLI v2
- 7 Studio vs CLI vs SDK – Comparison
- 8 Compute – Compute Resources
- 9 Datasets and Data Assets
- 10 Environments and Reproducibility
- 11 First End-to-End Job
- 12 Best Practices
- 13 Summary and Key Points
- 14 Glossary
- 15 Azure ML Studio — In-Depth Navigation
- 16 Notebooks in Studio — In-Depth Guide
- 17 Azure ML SDK v2 — Complete Guide
- 18 YAML Job Definitions
- 19 Components and Pipelines — Advanced Guide
More ML Platforms & Deployment courses
View all 10Introduction to Azure Machine Learning
Where Azure ML fits in the modern ML lifecycle, its common use cases and user workflows.
Azure ML Workspace Fundamentals
Azure ML workspace architecture, compute, datasets, environments, governance, security and cost.
Azure ML: Pipelines and Experiment Tracking
Build Azure ML pipelines, track experiments with MLflow and register and version the best model.
Azure ML: Practical Use Cases
Choose the right technique and run classification, clustering and batch inference with AutoML and the Designer.
Deploying Models with Azure Machine Learning
Online and batch endpoints, scoring scripts, blue/green deployment, AKS and model monitoring on Azure ML.
Deploying Machine Learning Solutions
Deploy models with Flask, on serverless, on Google AI Platform and to AWS SageMaker.
Interested in this course?
Contact us to book it or get a custom training plan for your team.