Implement a Machine Learning Solution with Azure Databricks – Intensive Training («DP314»)

• Identifying core workloads for Azure Databricks
• Using Data Governance tools Unity Catalog and Microsoft Purview
• Describing key concepts of an Azure Databricks solution
• Describing key elements of the Apache Spark architecture
• Using Spark to process and analyze data stored in files
• Preparing data for machine learning and training a model
• Using MLflow to log parameters, metrics, and other details from experiment runs
• Using the AutoML user interface in Azure Databricks
• Training a deep learning model in Azure Databricks

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

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.

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.

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.

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.

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

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.

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.

Data scientists and machine learning engineers

This course 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 this course.