Managing a workspace is a critical aspect of the DP-100 exam, which focuses on designing and implementing a data science solution on Azure. The primary way of interacting and managing these workspaces is by using developer tools such as Azure Machine Learning Studio, Azure Machine Learning SDK, and Azure CLI. These tools make it easier for developers to create, manage, and share workspaces and also enable you to interact with various elements of the workspace.
Azure Machine Learning Studio
Azure Machine Learning Studio is a visual interactive interface used for writing, debugging, and managing machine learning solutions.
To interact with a workspace, you do the following:
Navigate to Machine Learning Studio, click on your subscription, and select the desired workspace. There, you can:
- View, create and manage compute instances and compute clusters necessary for model training and inferencing.
- Monitor experiments and pipelines.
- Deploy models to different compute targets.
- Monitor running services.
For example, to create a compute instance within your workspace:
- Click “Compute” in the left pane in Azure Machine Learning studio.
- Under “Compute clusters”, click “+ New”.
- Enter the necessary details and click “Create”.
Azure Machine Learning SDK
Azure Machine Learning SDK is a Python library used for training and deploying Machine Learning models on Azure. You can interact with your workspace through the Azure Machine Learning SDK as follows:
python
from azureml.core import Workspace
ws = Workspace.create(name='myworkspace',
subscription_id='
resource_group='myresourcegroup',
create_resource_group=True,
location='eastus2'
)
The code snippet above creates a workspace named ‘myworkspace’ in the ‘myresourcegroup’ resource group.
The following developer tools are used to interact with the workspace:
- Workspace: Represents an Azure Machine Learning workspace for orchestrating machine learning workflows.
- Experiment: Represents an experiment, which is a named process within a workspace.
- Model: Represents a machine learning model that you have trained.
You can create, manage, and interact with these entities using their corresponding classes in the Azure Machine Learning SDK:
python
# Experiment example
from azureml.core import Experiment
exp = Experiment(workspace=ws, name='myexperiment')
# Model registration example
from azureml.core.model import Model
model = Model.register(model_path = "sklearn_regression_model.pkl",
model_name = "sklearn_regression_model",
tags = {'area': "diabetes", 'type': "regression"},
description = "Ridge regression model to predict diabetes",
workspace = ws)
Azure CLI
Azure Command-Line Interface (CLI) is a set of commands that allow you to create and manage Azure resources. You can use the Azure CLI to manage Azure Machine Learning workspaces.
To manage a workspace using Azure CLI, you can use the `az ml workspace` command. This command is available when you have installed the Azure Machine Learning extension.
For example, to create a workspace:
bash
az ml workspace create --workspace-name myworkspace --resource-group myresourcegroup
In conclusion, to manage a workspace effectively, you must understand and be able to use developer tools like Azure Machine Learning Studio, Azure Machine Learning SDK, and Azure CLI. These tools help streamline workspace creation, experiment management, model registration, and overall interaction with machine learning resources in the workspace. Understanding these will go a long way in helping you ace the DP-100: Designing and Implementing a Data Science Solution on Azure exam.
Practice Test
True or False: VS Code can be utilized as a workspace for developing and testing the code in Azure.
- True.
- False.
Answer: True.
Explanation: Visual Studio Code (VS Code) allows developers to write, test and debug their code. It includes built-in support for languages including Python, which is critical for data science activities on Azure.
Which tool does Azure use to manage the data science workspace?
- a) Jupyter Notebook
- b) Azure Machine Learning Studio
- c) VS Code
Answer: b) Azure Machine Learning Studio
Explanation: Azure Machine Learning Studio includes various tools for managing your workspace and implements solutions for data science. It allows experimentation, management of models, and monitoring of operations.
True or False: Azure DevOps cannot be used to interact with the workspace.
- True.
- False.
Answer: False.
Explanation: Azure DevOps provides developer services to support teams to plan work, collaborate on code development, and build and deploy applications.
Which of the following tools is not integrated in Azure for workspace interaction?
- a) GitHub
- b) Azure Machine Learning Studio
- c) Tableau
Answer: c) Tableau
Explanation: Tableau is not integrated for workspace interaction in Azure. Instead, GitHub and Azure Machine Learning Studio are commonly used tools.
True or False: You cannot manage resources in Azure Resource Group.
- True.
- False.
Answer: False.
Explanation: An Azure Resource Group is a container that holds related resources for an Azure solution. The group includes those resources that you want to manage as a group.
Single Choice: _______ allows developers to manage and track their machine learning models in Azure workspace.
- a) Python SDK
- b) R SDK
- c) Both a and b
Answer: c) Both a and b
Explanation: Both Python SDK and R SDK allow developers to manage and track their ML models in Azure workspace.
True or False: Azure enables developers to write, debug, and test code in their language of choice.
- True.
- False.
Answer: True.
Explanation: Azure supports a range of programming languages such as Python, JavaScript, Java, .NET, allowing developers to use their preferred languages.
In the updated Azure Machine Learning Studio, you do not need a web browser to start working on your workspace.
- a) True
- b) False
Answer: b) False
Explanation: Azure Machine Learning Studio is a web-based, integrated development environment for developing data experiments. It requires a web browser.
True or False: You can use Azure Portal to create and manage your workspace.
- True.
- False.
Answer: True.
Explanation: Azure Portal is a web-based application which allows you to create, manage, and monitor everything from simple web apps to complex cloud applications in a single, unified console.
Which of the following can be used to interact with Azure workspace locally?
- a) Python SDKs
- b) Azure Machine Learning Studio
- c) R SDKs
- d) Both a and c
Answer: d) Both a and c
Explanation: Azure provides Python and R SDKs to allow local interaction with the Azure workspace. Azure Machine Learning Studio is a web-based platform and doesn’t allow local interaction.
True or False: It is not necessary to use developer tools for workspace interaction in Azure.
- True.
- False.
Answer: False.
Explanation: Developer tools are critical for pipeline management, data wrangling, model training, deployment, and other functions in Azure.
The Python SDK does not support workspace interaction in Azure.
- a) True
- b) False
Answer: b) False
Explanation: Python SDK in Azure is used for developing, testing and deploying machine learning models.
What can you use to explore your datasets in Azure Machine Learning Studio?
- a) Data Explorer
- b) Data Palette
- c) Data Finder
- d) None of the above
Answer: a) Data Explorer
Explanation: Data Explorer in Azure Machine Learning Studio gives a detailed description of the data, helping you understand data structures, relationships, and anomalies.
Visual Studio Code does not provide support for Python in Azure.
- a) True
- b) False
Answer: b) False
Explanation: Visual Studio Code is a lightweight but powerful source code editor which runs on your desktop and is available for Windows, macOS and Linux. It comes with built-in support for Python to develop and deploy machine learning models on Azure.
True or False: Azure Machine Learning Studio supports version control.
- True.
- False.
Answer: True.
Explanation: Azure Machine Learning Studio supports version control. This allows for tracking of changes and the ability to revert to previous versions of the code.
Interview Questions
What is the main feature of Azure DevTest Labs in managing workspaces for development?
Azure DevTest Labs enables developers to quickly create environments in Azure, which supports managing workspaces by significantly reducing the setup and teardown time of their environments.
How does Microsoft Azure Notebooks support workspace management for developers?
Microsoft Azure Notebooks provide a secure and collaborative environment for developers to build and run Jupyter notebooks, allowing them to manage code, datasets, and other resources needed in their workspace.
How can developers manage their workspace using Azure Resource Manager?
Azure Resource Manager allows developers to manage infrastructure through declarative templates and configuration scripts. This means developers can consistently deploy, organize, and manage resources in their workspace.
What tool inside Azure DevOps allows developers to track changes made to their code?
Azure Repos is a set of version control tools inside Azure DevOps that allows developers to manage and track changes made to their development workspace.
What is the main function of Azure Pipelines in workspace management?
Azure Pipelines provides build and release services to support continuous integration and delivery of applications, enabling developers to manage application deployment in their workspace.
How would a data scientist use Azure Machine Learning Studio for workspace interaction?
A data scientist would use Azure Machine Learning Studio for workspace interaction by developing, training, and deploying machine learning models in a simple drag-and-drop interface.
What is the role of Azure Boards in workspace management?
Azure Boards is a tool that helps in tracking work items. It supports information management within the workspace, allowing developers to manage projects, stories, or tasks effectively.
How can developers interact with their workspace using Azure Cloud Shell?
Azure Cloud Shell is a browser-based shell that provides developers with secure and authenticated access to their Azure resources, allowing them to interact with and manage their workspace from anywhere.
Which Azure service provides developers with integrated development environments in the cloud?
Azure Dev Spaces provides integrated development environments in the cloud, allowing developers to iterate and debug code quickly in the context of their larger application.
What tool within Azure DevOps Services allows developers to plan, track, and discuss work across the teams?
Azure Boards, a feature within Azure DevOps Services, allows developers to plan, track, and discuss work across the whole team.
In the context of Azure Machine Learning workspaces, what can workspace resources include?
In Azure Machine Learning, workspace resources can include datasets, experiments, compute targets, model deployments, and pipelines.
How can you use the Azure portal to manage a workspace?
The Azure portal is a web-based unified console that provides an alternative to command-line tools. With the Azure portal, you can manage your Azure workspace, configure and tweak performance settings, and automate tasks.
What value does Azure Monitor add in managing workspaces in Azure?
Azure Monitor provides full-stack observability into your applications, infrastructure, and network, allowing you to understand how your applications are performing and proactively identify issues affecting them and the resources they depend on.
How can Azure Databricks be used to manage a workspace?
Azure Databricks is an Apache Spark-based analytics platform. Users can manage their workspace effectively by creating notebooks, libraries, and clusters, and scheduling jobs all in one place.
What is the role of Azure Artifacts in workspace management?
Azure Artifacts is an integrated package management solution within Azure pipelines. It enables developers to create, host, manage, and share packages across the team, aiding in workspace management.
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