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Build Machine Learning Solutions Using Azure Databricks

Learn to use Azure Databricks to implement machine learning solutions at scale.

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    Prerequisites

    • Experience using Python to explore data and train machine learning models with open-source frameworks like Scikit-Learn, PyTorch, or TensorFlow. 
    • Basic understanding of machine learning concepts and familiarity with data analysis libraries (like pandas or NumPy). 
    • Foundational knowledge of Azure cloud services. 
    • Experience with Apache Spark is helpful but not mandatory.

    What You Will Learn

    • Learn the basics of Azure Databricks and Apache Spark.
    • Run and manage Spark-based data analytics at scale.
    • Train models using common ML frameworks in Databricks.
    • Use MLflow for experiment tracking and model management.
    • Optimize models with automatic hyperparameter tuning.
    • Build and deploy models using AutoML and deep learning tools.
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    Curriculum

    1.

    Learning Objective:

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

    Topics
    • Introduction
    • Get started with Azure Databricks
    • Identify Azure Databricks workloads
    • Understand key concepts
    • Data governance using Unity Catalog and Microsoft Purview
    • Exercise - Explore Azure Databricks
    • Module assessment
    • Summary
    2.

    Learning Objective:

    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.


    Topics
    • Introduction
    • Get to know Spark
    • Create a Spark cluster
    • Use Spark in notebooks
    • Use Spark to work with data files
    • Visualize data
    • Exercise - Use Spark in Azure Databricks
    • Module assessment
    • Summary
    3.

    Learning Objective:

    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.

    Topics
    • Introduction
    • Understand principles of machine learning
    • Machine learning in Azure Databricks
    • Prepare data for machine learning
    • Train a machine learning model
    • Evaluate a machine learning model
    • Exercise - Train a machine learning model in Azure Databricks
    • Module assessment
    • Summary
    4.

    Learning Objective:

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

    Topics
    • Introduction
    • Capabilities of MLflow
    • Run experiments with MLflow
    • Register and serve models with MLflow
    • Exercise - Use MLflow in Azure Databricks
    • Module assessment
    • Summary
    5.

    Learning Objective:

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

    Topics
    • Introduction
    • Optimize hyperparameters with Optuna
    • Review trials
    • Scale hyperparameter optimization
    • Exercise - Optimize hyperparameters for machine learning in Azure Databricks
    • Module assessment
    • Summary
    6.

    Learning Objective:

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

    Topics
    • Introduction
    • What is AutoML?
    • Use AutoML in the Azure Databricks user interface
    • Use code to run an AutoML experiment
    • Exercise - Use AutoML in Azure Databricks
    • Module assessment
    • Summary
    7.

    Learning Objective:

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


    Topics
    • Introduction
    • Understand deep learning concepts
    • Train models with PyTorch
    • Distribute PyTorch training with TorchDistributor
    • Exercise - Train deep learning models on Azure Databricks
    • Module assessment
    • Summary
    8.

    Learning Objective:

    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.

    Topics
    • Introduction
    • Automate your data transformations
    • Explore model development
    • Explore model deployment strategies
    • Explore model versioning and lifecycle management
    • Exercise - Manage a machine learning model
    • Module assessment
    • Summary

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    Frequently Asked Questions

    Yes, you will experience KnowledgeHut's immersive learning in an on-demand format. This will include e-learning material to help you:

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    Yes, our online course is designed to give you flexibility to skill up as per your convenience. The course is delivered in a Self-Paced mode so that you can balance your work and learning as per your schedule.

    Yes! Upon passing this online course, you will receive a signed certificate of completion from KnowledgeHut. Thousands of KnowledgeHut alumni use their course certificate to demonstrate skills to employers and their networks.

    KnowledgeHut’s online courses is well-regarded by industry experts, who contribute to our curriculum and use our tech programs to train their own teams.

    You can cancel your enrolment and receive refunds in line with our Cancellations and Refunds policy found at https://www.knowledgehut.com/refund-policy. 

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