Machine learning with Azure Databricks (DP-3014)

Embark on an enriching journey with this hands-on instructor-led Microsoft course, 'Machine Learning with Azure Databricks (DP-3014),' designed to empower you with cloud-scale capabilities for data analytics and machine learning. Within this immersive one-day experience, you'll delve into Azure Databricks, a versatile platform enabling data scientists and machine learning engineers to implement robust solutions at scale, revolutionizing the way data insights are extracted and utilized.

Machine learning with Azure Databricks (DP-3014) Benefits

• In this course, you will learn how to:

  • Gain proficiency in utilizing Azure Databricks, a cloud service offering a scalable platform for data analytics using Apache Spark. 
  • Acquire practical knowledge and hands-on experience in employing Spark to transform, analyze, and visualize data at scale. 
  • Develop skills in training machine learning models and evaluating their performance within the Azure Databricks environment. 
  • Learn to leverage MLflow, an open-source platform for managing the machine learning lifecycle, seamlessly integrated with Azure Databricks. 
  • Master the art of hyperparameter tuning and optimization using Hyperopt library, enhancing the efficiency of machine learning workflows. 
  • Explore the simplicity and effectiveness of AutoML in Azure Databricks for automating the model building process. 
  • Dive into the realm of deep learning, understanding concepts and training models for complex AI workloads like forecasting, computer vision, and natural language processing. 

• Training Prerequisites

  To fully benefit from this course, please ensure you possess proficiency in Python for data exploration and machine learning model training using popular open-source frameworks such as Scikit-Learn, PyTorch, and TensorFlow.  

Machine learning with Azure Databricks Training Outline

Learning Objectives

1. Explore Azure Databricks

• Introduction to Azure Databricks as a cloud service providing a scalable platform for data analytics.
• Use of Apache Spark in Azure Databricks for performing data transformations, analysis, and visualizations at scale.

2. Train a Machine Learning Model in Azure Databricks

• Understanding how data is used for training predictive models in Azure Databricks.
• Overview of the commonly used machine learning frameworks supported by Azure Databricks.

3. Use MLflow in Azure Databricks

• Introduction to MLflow as an open-source platform managing the machine learning lifecycle.
• Insight into how MLflow is natively supported in Azure Databricks.

4. Tune Hyperparameters in Azure Databricks

• The important role of tuning hyperparameters in machine learning.
• Using the Hyperopt library in Azure Databricks for automated hyperparameters optimization.

5. Use AutoML in Azure Databricks

• An overview of AutoML’s role in simplifying the process of building effective machine learning models.
• Insight into how AutoML fits into the Azure Databricks ecosystem.

6. Train Deep Learning Models in Azure Databricks

• Understanding deep learning and its use of neural networks for training machine learning models.
• Looking at the complex forecasting, computer vision, natural language processing, and other AI workloads handled by deep learning in Azure Databricks.