Manage a workspace by using developer tools for workspace interaction

To effectively manage a workspace for your data science solution on Azure, it is beneficial to leverage developer tools that provide seamless interaction and enhance productivity. These tools can assist in various tasks, such as code development, collaboration, experimentation, and visualization. In this article, we will explore some essential developer tools specifically designed for managing a workspace in Azure for your data science solution.

Azure Machine Learning SDK

The Azure Machine Learning SDK is a comprehensive set of Python libraries that simplifies the process of building, training, deploying, and managing machine learning models on Azure. It provides a high-level interface to interact with Azure Machine Learning services and offers functionalities like dataset management, experiment tracking, and deploying models as web services. To get started, you need to install the SDK using the following command:

!pip install azureml-sdk

Jupyter Notebooks

Jupyter Notebooks are a popular tool for interactive coding and data exploration. They provide a web-based interface where you can write code, visualize data, and document your work. Azure Machine Learning integrates seamlessly with Jupyter Notebooks, allowing you to create, run, and manage notebooks directly in your workspace. You can use the azureml-sdk library to create a new Jupyter Notebook server in Azure Machine Learning environment:

from azureml.core import Workspace
ws = Workspace.from_config()

try:
compute_target = ComputeTarget(workspace=ws, name=compute_target_name)
print('Found existing compute target')
except ComputeTargetException:
compute_target_config = ComputeTarget.provisioning_configuration(
vm_size='STANDARD_D2_V2',
min_nodes=0,
max_nodes=1
)
compute_target = ComputeTarget.create(ws, compute_target_name, compute_target_config)
compute_target.wait_for_completion(show_output=True)

Azure Databricks

Azure Databricks is an Apache Spark-based analytics platform that provides a collaborative environment for data scientists. It allows you to create and run Python, Scala, and SQL notebooks to perform data exploration, data preparation, and model training. Azure Databricks seamlessly integrates with Azure Machine Learning, enabling you to train and deploy machine learning models at scale. To create an Azure Databricks workspace, use the Azure portal or Azure CLI.

Visual Studio Code

Visual Studio Code is a lightweight and extensible code editor that supports a wide range of programming languages. It provides excellent tools and extensions for Python and data science development. With the Azure Machine Learning extension for Visual Studio Code, you can easily manage your workspace, create and submit experiments, and monitor their progress directly from your editor. Install the extension from the Visual Studio Code marketplace and connect to your Azure Machine Learning workspace using the provided interface.

Azure DevOps

Azure DevOps is a set of development tools that facilitate the collaboration and automation of software development workflows. It provides version control, continuous integration/continuous deployment (CI/CD), and project management capabilities. You can use Azure DevOps to version control your code, automate the training and deployment pipeline of your data science solution, and track the experiments and model artifacts. Refer to the Azure DevOps documentation for detailed guidance on setting up your data science project in Azure DevOps.

These developer tools in Azure offer powerful features that significantly enhance the productivity and collaboration of your data science teams. By leveraging these tools, you can streamline your workspace management tasks, iterate on your models, and build robust and scalable data science solutions on Azure.