Path1.Mod2.a - Explore Workspace Developer Tools - ML Studio

Question 1

The three tools for Azure ML and which to use…

Answer 1

• ML Studio
• Python SDK
• Azure CLI

ALL are a personal preference…so use whatever

Question 2

Ideal use cases for ML Studio

Answer 2

• When you want to use a no-code approach
• Quickly review your work and its results
• Submit jobs and manage models from a Jupyter notebook
• Ideal for data scientists.

Question 3

Ideal use cases for Azure ML Python SDK

Answer 3

• Prefer the Python code approach and automating repetative work
• Submit jobs and manage models from a Jupyter notebook
• Ideal for data scientists

Question 4

Ideal use cases for Azure CLI

Answer 4

• Prefer the code approach and automating repetative work (uses the Azure Machine Learning extension)
• Ideal for infrastructure automation

Question 5

When to use the Azure ML v2 and some of its features

Answer 5

Whenever you’re starting a new Machine Learning project or workflow!!
V2 has new features:
* Managed Inferencing
* Reusable pipeline components
* Improved pipeline scheduling
* Responsible AI Dashboard
* Assets Registry

Question 6

Azure ML v2-created Workspaces cannot reuse Azure ML v1-created entities (Workspaces, Compute, Models, Environments) due to incompatibility (T/F)

Answer 6

False. v2 can use any of them.

Question 7

Au-j As-a Co-c

The three main Menus (left-hand side) in Azure ML Studio and what they consist of wrt Models

Answer 7

• Authoring - Create new jobs to train and track an ML Model
• Assets - Create and review assets used for training models
• Manage - Create and manage compute resources needed to train Models

Question 8

D AML

The two Authoring tools for creating a new Job in the Studio

Answer 8

• Designer - Drag n Drop interface for creating Pipelines with prebuilt (custom) Components
• Auto Machine Learning (AutoML) - Wizard interface that lets you train a model using a combination of algorithms and data preproessing techniques to find the best Model for your data.

Question 9

Notebook Kernels

Type of Compute ideal for development and why …

Answer 9

Compute Instances are ideal for dev because they are more scalable and cost efficient than local training

Question 10

Type of Compute ideal for training models and why…

Answer 10

Compute Clusters are ideal for training because the Cluster will dynamically resize with its number of nodes in order to run a training job, then go back to zero nodes once the Cluster isn’t needed anymore