DP-3014: Implementing a Machine Learning Solution with Azure Databricks

Course Description

Unlock the power of Azure Databricks to build scalable, production-ready machine learning solutions. In this intensive, instructor-led course, you'll learn to leverage Apache Spark, MLflow, AutoML, and Hyperopt within the Azure ecosystem. Designed for data scientists and machine learning engineers, this course covers everything from data preprocessing and model training to hyperparameter tuning and deployment. By the end, you'll be equipped to implement end-to-end machine learning workflows using Azure Databricks.

Target Audience

This course is ideal for:

• Data Scientists and Machine Learning Engineers seeking to implement machine learning solutions at scale using Azure Databricks.

• Professionals aiming to integrate Apache Spark and MLflow into their machine learning workflows.

• Individuals preparing for roles that involve building and deploying machine learning models in the Azure cloud environment.

Prerequisites:

• Proficiency in Python for data exploration and model training.

• Familiarity with machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow.

• Understanding of basic machine learning concepts and workflows.

Course Outline

Module 1: Explore Azure Databricks

• Provision and navigate an Azure Databricks workspace.

• Identify key workloads and personas in Azure Databricks.

• Understand the architecture and components of Azure Databricks.

Module 2: Use Apache Spark in Azure Databricks

• Create and configure Apache Spark clusters.

• Utilize Spark for data processing and analysis.

• Visualize data using Spark's built-in capabilities.Module 3: Train a Machine Learning Model in Azure Databricks

• Prepare data for machine learning tasks.

• Train machine learning models using MLlib.

• Evaluate model performance and accuracy.

Module 4: Use MLflow in Azure Databricks

• Track experiments and log metrics with MLflow.

• Register and manage models using MLflow's Model Registry.

• Deploy models for inference within Azure Databricks.

Module 5: Tune Hyperparameters in Azure Databricks

• Implement hyperparameter optimization using Hyperopt.

• Distribute hyperparameter tuning tasks across multiple nodes.

• Analyze and select the best-performing model configurations.

Module 6: Use AutoML in Azure Databricks

• Leverage AutoML capabilities for automated model selection and tuning.

• Run AutoML experiments via the Azure Databricks UI and API.

• Interpret AutoML results and integrate them into workflows.

Module 7: Train Deep Learning Models in Azure Databricks

• Understand deep learning concepts and architectures.

• Build and train deep learning models using PyTorch.

• Distribute training workloads using Horovod for parallel processing.

Module 8: Manage Machine Learning in Production with Azure Databricks

• Automate feature engineering and data pipelines.

• Implement model deployment strategies for real-time inference.

• Monitor and manage models in production environments.

Hands-On Experience

Approximately 40–50% of the course is dedicated to hands-on exercises, allowing participants to:

• Provision and configure Azure Databricks workspaces and clusters.

• Develop and train machine learning models using Apache Spark and MLlib.

• Utilize MLflow for experiment tracking and model management.

• Optimize models through hyperparameter tuning with Hyperopt.

• Implement AutoML workflows for automated model development.

• Build and train deep learning models using PyTorch and Horovod.

• Deploy and manage machine learning models in production environments.