DP-3014 - Implement a Machine Learning solution with Azure Databricks

Azure Databricks is a cloud-scale platform for data analytics and machine learning. Data scientists and machine learning engineers can use Azure Databricks to implement machine learning solutions at scale.

Duration: 1.0 day

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Schedule

Virtual ILT | 30 Apr 2024 - 30 Apr 2024 India

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Objectives

  • Provision an Azure Databricks workspace.
  • Identify core workloads and personas for Azure Databricks.
  • Describe key concepts of an Azure Databricks solution.
  • 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.
  • Prepare data for machine learning
  • Train a machine learning model
  • Evaluate a machine learning model
  • Use MLflow to log parameters, metrics, and other details from experiment runs.
  • Use MLflow to manage and deploy trained models.
  • Use the Hyperopt library to optimize hyperparameters.
  • Distribute hyperparameter tuning across multiple worker nodes.
  • Use the AutoML user interface in Azure Databricks
  • Use the AutoML API in Azure Databricks
  • Train a deep learning model in Azure Databricks
  • Distribute deep learning training by using the Horovod library

Content

1. Explore Azure Databricks

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

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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.

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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.

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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.

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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.

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6. Use AutoML in Azure Databricks

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

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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.

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Audience

Data scientists and machine learning engineers who want to use Azure Databricks for machine learning solutions at scale.

Prerequisites

Before starting this module, you should be familiar with machine learning on Azure Databricks.

Certification

product-certification

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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