Course Overview

DP-3014: Implementing a Machine Learning Solution with Azure Databricks is intended to give data professionals the know-how to use Azure Databricks to create and implement scalable machine learning solutions. This course guides you through the whole machine learning lifecycle, from data preparation and model training to deployment and monitoring, all in a collaborative, cloud-based setting, utilizing the capabilities of Apache Spark, MLflow, and Azure Machine Learning. 
 
You will learn how to manage trials, operationalize ML models in production, and streamline data operations through practical laboratories and real-world scenarios. This course will help you become proficient in the tools and methods required to use Azure’s unified analytics ecosystem to produce significant AI solutions, regardless of your background as a data scientist, machine learning engineer, or data engineer. 

After completing this course of training , you will be able to :

  • Design and implement scalable machine learning workflows using Azure Databricks and Apache Spark
  • Prepare, clean, and transform large datasets for machine learning tasks
  • Build, train, and evaluate machine learning models using Python and popular ML libraries
  • Track and manage machine learning experiments using ML flow
  • Deploy models to Azure Machine Learning for real-time or batch inference

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Key Features:

  • Comprehensive Coverage: Learn the whole machine learning lifecycle with Azure Databricks, MLflow, and Azure Machine Learning, from data intake and preprocessing to model deployment and monitoring. 

  • Hands-on Practical Exercises:  Use what you learn in practical laboratories and supervised activities that strengthen your ability to develop and implement machine learning solutions in a cloud setting. 

  • Focus on role-based learning: With information specifically tailored to real-world employment positions and Azure certification pathways, the course is designed for data scientists, machine learning engineers, and data engineers. 

  • Career Advancement: Open doors to advanced analytics, AI positions, and cloud-native ML projects by arming yourself with in-demand Azure ML and Databricks capabilities that are essential for contemporary data teams. 

Who should Attend?

  • Data Scientists looking to operationalize their ML models using cloud-native tools and frameworks
  • Machine Learning Engineers are responsible for automating and scaling ML pipelines in production environments
  • Data Engineers aiming to integrate ML workflows into data pipelines using Apache Spark and Azure services
  • AI/ML Developers who want hands-on experience with tools like MLflow and Azure Machine Learning
  • Technical Architects and Consultants involved in designing AI/ML solutions on the Azure platform

Prerequisites:

  • Basic understanding of machine learning
  • Familiarity with Python programming
  • Experience with data analysis tools
  • Foundational knowledge of Azure services
  • Comfort with working in cloud-based environments

    • Learning objectives of the course

    • Understand the architecture and capabilities of Azure Databricks for machine learning workloads
    • Prepare and process data at scale using Apache Spark within Azure Databricks
    • Build and train machine learning models using Python, Spark MLlib, and popular ML libraries
    • Track, manage, and compare experiments using MLflow
    • Register, deploy, and manage models using Azure Machine Learning
    • Implement automated machine learning workflows and pipeline

    Why choose CloudThat as your training partner?

    • Expert Instructors: CloudThat’s instructors are highly experienced and certified professionals with in-depth knowledge of Databricks and Machine learning. They provide top-notch training and guidance throughout the preparation process.
    • Comprehensive Course Content: CloudThat offers a well-structured and comprehensive course curriculum that covers all the essential topics needed to excel in the Databricks and Machine learning concepts.
    • Hands-on Labs: CloudThat emphasizes hands-on learning through practical exercises and real-world scenarios, allowing candidates to gain practical experience working with Databricks Unified Analytics Platform with Machine learning.
    • Flexibility: CloudThat offers flexible training options, including online and in-person classes, allowing candidates to choose the mode of learning that suits their schedule and preferences.
    • Track Record: CloudThat has a proven track record of success in training and preparing candidates for various cloud and data certifications, including Databricks.

    Course Outline Download Course Outline

    • Get started with Azure Databricks
    • Identify Azure Databricks workloads
    • Understand key concepts
    • Data governance using Unity Catalog and Microsoft Purview
    • Lab - Explore Azure Databricks

    • Get to know Spark
    • Create a Spark cluster
    • Use Spark in notebooks
    • Use Spark to work with data files
    • Visualize data
    • Lab - Use Spark in Azure Databricks

    • 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
    • Lab - Train a machine learning model in Azure Databricks

    • Capabilities of MLflow
    • Run experiments with MLflow
    • Register and serve models with MLflow
    • Lab - Use MLflow in Azure Databricks

    • Optimize hyperparameters with Hyperopt
    • Review Hyperopt trials
    • Scale Hyperopt trials
    • Lab - Optimize hyperparameters for machine learning in Azure Databricks

    • What is AutoML?
    • Use AutoML in the Azure Databricks user interface
    • Use code to run an AutoML experiment
    • Lab - Use AutoML in Azure Databricks

    • Understand deep learning concepts
    • Train models with PyTorch
    • Distribute PyTorch training with TorchDistributor
    • Lab - Train deep learning models on Azure Databricks

    • Automate your data transformations
    • Explore model development
    • Explore model deployment strategies
    • Explore model versioning and lifecycle management
    • Lab - Manage a machine learning model

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    Course ID: 25170

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    FAQs:

    The practical course DP-3014: Implementing a Machine Learning Solution with Azure Databricks teaches data professionals how to use Azure Databricks to create, train, and implement machine learning models at scale.

    This course is ideal for data scientists, machine learning engineers, data engineers, AI/ML developers, and technical architects who want to build, deploy, and manage scalable machine learning solutions using Azure Databricks and related Azure services.

    Yes. Participants should have a basic understanding of machine learning concepts, familiarity with Python programming, experience with data analysis libraries (like pandas or NumPy), and foundational knowledge of Azure cloud services. Experience with Apache Spark is helpful but not mandatory.

    By the end of the course, you will be able to: Prepare and process data at scale with Apache Spark on Azure Databricks Build, train, and evaluate machine learning models using Python and Spark MLlib Track experiments using MLflow Deploy and manage models with Azure Machine Learning

    While not always mandatory, having an Azure subscription is helpful for hands-on labs. Many training providers offer temporary access to Azure environments or sandbox accounts during the course.

    The course typically includes a mix of instructor-led sessions, hands-on labs, and practical exercises to ensure applied learning.

    Yes, DP-3014 aligns with Microsoft’s role-based certification paths and helps prepare for certifications focused on Azure data and AI workloads.

    Enquire Now