Implementing a Machine Learning solution with Azure Databricks

This course is destinated to

• Data Scientists
• Data Engineers
• Data Analysts
• Machine Learning Engineers
• AI Developers
• Software Developers
• Cloud Solution Architects
• IT Managers and Decision Makers
• Business Intelligence Developers
• Anyone interested in learning about implementing machine learning solutions using Azure Databricks.

• Explore Azure Databricks
• Use Apache Spark in Azure Databricks
• Train a machine learning model in Azure Databricks
• Use MLflow in Azure Databricks
• Tune hyperparameters in Azure Databricks
• Use AutoML in Azure Databricks
• Train deep learning models in Azure Databricks
• Manage machine learning in production with Azure Databricks

Module 1: Explore Azure Databricks

Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.

• Provision an Azure Databricks workspace.
• Identify core workloads and personas for Azure Databricks.
• Describe key concepts of an Azure Databricks solution.

Module 2: Use Apache Spark in Azure Databricks

Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.

• Describe key elements of the Apache Spark architecture.
• Create and configure a Spark cluster.
• Describe use cases for Spark.
• Use Spark to process and analyze data stored in files.
• Use Spark to visualize data.

Module 3: Train a machine learning model in Azure Databricks

Machine learning involves using data to train a predictive model. Azure Databricks support multiple commonly used machine learning frameworks that you can use to train models.

• Prepare data for machine learning
• Train a machine learning model
• Evaluate a machine learning model

Module 4: Use MLflow in Azure Databricks

MLflow is an open source platform for managing the machine learning lifecycle that is natively supported in Azure Databricks.

• Use MLflow to log parameters, metrics, and other details from experiment runs.
• Use MLflow to manage and deploy trained models.

Module 5: Tune hyperparameters in Azure Databricks

Tuning hyperparameters is an essential part of machine learning. In Azure Databricks, you can use the Hyperopt library to optimize hyperparameters automatically.

• Use the Hyperopt library to optimize hyperparameters.
• Distribute hyperparameter tuning across multiple worker nodes.

Module 6: Use AutoML in Azure Databricks

AutoML in Azure Databricks simplifies the process of building an effective machine learning model for your data.

• Use the AutoML user interface in Azure Databricks
• Use the AutoML API in Azure Databricks

Module 7: Train deep learning models in Azure Databricks

Deep learning uses neural networks to train highly effective machine learning models for complex forecasting, computer vision, natural language processing, and other AI workloads.

• Train a deep learning model in Azure Databricks
• Distribute deep learning training by using the Horovod library

Module 8: Manage machine learning in production with Azure Databricks

Machine learning enables data-driven decision-making and automation, but deploying models into production for real-time insights is challenging. Azure Databricks simplifies this process by providing a unified platform for building, training, and deploying machine learning models at scale, fostering collaboration between data scientists and engineers.

• Automate feature engineering and data pipelines
• Model development and training
• Model deployment strategies
• Model versioning and lifecycle management
  
  

This learning path assumes that you have experience of using Python to explore data and train machine learning models with common open source frameworks, like Scikit-Learn, PyTorch, and TensorFlow. Consider completing the Create machine learning models learning path before starting this one.