• 9 minutes

When you start developing and training machine learning models, you can use Azure Databricks as it provides powerful data processing capabilities and a collaborative environment.

First, let's explore the features in Azure Databricks that help you during model development and training. After, you can explore some features that help you and your team work and collaborate efficiently.

Develop machine learning models with Azure Databricks

During model development, you can use various features available in Azure Databricks to:

• Automate algorithm selection and hyperparameter tuning.
• Track model training experiments.
• Manage machine learning models.
• Assess model performance and accuracy.
• Deploy and integrate your model.

Let's explore each of these features.

Automate algorithm selection

During development, you want to experiment with different algorithms and hyperparameters to understand which configuration results in the best machine learning model.

To quickly and easily automate the selection of algorithms, hyperparameter tuning, and model evaluation, you can use Automated Machine Learning (AutoML).

AutoML simplifies the model development process and enables you to focus on interpreting results and making data-driven decision.

Perform hyperparameter tuning

Hyperparameter tuning is a critical step in optimizing machine learning models, and Azure Databricks provides tools to streamline this process.

Next to using AutoML to automatically perform hyperparameter tuning for you, you can also use Hyperopt to efficiently explore different hyperparameter configurations and identify the best-performing models.

By optimizing model training through hyperparameter tuning, you can enhance model accuracy and performance.

Track model training with experiments

In Azure Databricks, you can train and evaluate machine learning models using popular frameworks such as scikit-learn, TensorFlow, and PyTorch.

You can also train models on distributed computing cluster, significantly reducing training time when you use large datasets or compute-intensive algorithms.

To more effectively develop models, you can track the models you train by using experiments through an integration with MLflow, an open-source framework for managing the complete machine learning lifecycle.

MLflow provides functionalities for tracking experiments, packaging code, and sharing models, ensuring reproducibility and collaboration throughout the development process.

An experiment contains all the necessary metadata to reproduce your model training workload, including all inputs and outputs. The outputs can include various metrics and visualization to assess the model's performance for that experiment. When you track model training, you can easily compare different models you trained, using different configurations, to find the model that best fits your needs.

Work and collaborate efficiently in Azure Databricks

When you use Azure Databricks for the end-to-end lifecycle of machine learning workloads, you can benefit from various features that allow you to work and collaborate more efficiently.

Collaborate on code in a workspace

Azure Databricks offers a collaborative workspace where data scientists and engineers can work together in a unified environment.

The platform supports various programming languages, including Python, R, Scala, and SQL, allowing you and your team members to use your preferred tools and languages. The collaborative environment enhances productivity and fosters teamwork, as you can share notebooks, visualizations, and insights.

Manage your code with version control

Using version control is essential for managing changes to your code and collaborating with your team.

Azure Databricks integrates with Git, allowing you to version your notebooks and scripts. By connecting your Databricks workspace to a Git repository, you can track changes, revert to previous versions, and collaborate more effectively with your team.

To set up Git integration in Azure Databricks:

1. Connect to a Git repository: In your Databricks workspace, go to the User Settings and configure your Git provider (for example, GitHub, GitLab, Bitbucket). Authenticate with your Git credentials and connect to your repository.
2. Clone a repository: Use the Databricks UI to clone a repository into your workspace. Cloning to a repository allows you to work on the code directly within Databricks and commit changes back to the repository.
3. Commit and push changes: After making changes to your notebooks or scripts, use the Git integration to commit and push your changes to the remote repository. Using the Git integrations ensures that your work is versioned and backed up.

Implement Continuous Integration and Continuous Deployment (CI/CD)

Azure Databricks supports CI/CD practices for machine learning models, enabling you to automate the deployment and monitoring of models. By integrating with tools like Azure DevOps and GitHub Actions, you can implement automated pipelines that ensure models are continuously tested, validated, and updated. This capability is essential for maintaining the accuracy and reliability of models in production environments.

Azure Databricks provides a comprehensive and scalable platform for model development and training. Its collaborative workspace, advanced data processing capabilities, and seamless integration with other Azure services make it an ideal choice for data scientists and engineers looking to build and deploy high-performing machine learning models.