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DP-100: Designing and Implementing a Data Science Solution on Azure Course

Class Dates
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(click Enroll for locations)
4/28-5/1
Class Dates and Times
04/28/2025Monday 10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
04/29/2025Tuesday 10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
04/30/2025Wednesday10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
05/01/2025Thursday 10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT

Ready to Run Class
A Ready to Run date will not cancel due to low enrollments. Seats are still available but filling fast - Register soon!
Last Reg Date4/23
 
5/27-5/30
Class Dates and Times
05/27/2025Tuesday 10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
05/28/2025Wednesday10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
05/29/2025Thursday 10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
05/30/2025Friday 10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
 
6/24-6/27
Class Dates and Times
06/24/2025Tuesday 10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
06/25/2025Wednesday10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
06/26/2025Thursday 10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
06/27/2025Friday 10:00-4:45 EDT
  9:00-3:45 CDT
  8:00-2:45 MDT
  7:00-1:45 PDT
 
Fee:  $2295

Savings options:

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  1. Home
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  4. DP-100: Designing and Implementing a Data Science Solution on Azure

DP-100: Designing and Implementing a Data Science Solution on Azure Course

 

Course Overview

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. The DP-100 class teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

Audience profile

This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.

Prerequisites

Before attending this DP-100 course, students must have:
• Azure Fundamentals Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
• Python programming ability and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.

Interested in Getting Certification? Check out our Exam Pak option!

Prepare for Microsoft Certification by getting our Exam Pak -- a $500 value you can get for free! Optional Exam Pak includes:

• 24/7 Online Support

Need assistance while you are learning? Chat with our 24/7 online support specialists. And, with your permission, the expert can even take over your computer to provide assistance. (90-day access.)

• Microsoft Exam Reference Guide

When you are ready for certification, begin your preparation with the Exam Reference Guide from Microsoft Press. We provide you with a copy of this book that focuses on the critical skills and knowledge measured on the Microsoft Certification exam.

• Practice Exam Software

You may study at your own pace with this web-based practice exam. Exam-like questions are designed to help you prepare for your certification exam by validating your knowledge and reinforcing key concepts.

• Exam Voucher with Exam-Pass Guarantee

Prepare for your exam using the practice software. Once you have achieved an 85% or above score, contact us and we will provide you with an exam voucher. Didn't pass the first time? Not a problem--you will get a second voucher with our Exam-Pass Guarantee - For details visit Exam Pass Guarantee

For full details on this promotional offer visit Free Microsoft Exam Pak

**Call our office to order the Exam Pak when registering for this class. Exam Paks are only available with this promotion when you register for your class. This promotion is valid on new registrations only and cannot be combined with other offers.**

COURSE OUTLINE

1 - Design a data ingestion strategy for machine learning projects

  • Identify your data source and format
  • Choose how to serve data to machine learning workflows
  • Design a data ingestion solution

2 - Design a machine learning model training solution

  • Identify machine learning tasks
  • Choose a service to train a machine learning model
  • Decide between compute options

3 - Design a model deployment solution

  • Understand how model will be consumed
  • Decide on real-time or batch deployment

4 - Design a machine learning operations solution

  • Explore an MLOps architecture
  • Design for monitoring
  • Design for retraining

5 - Explore Azure Machine Learning workspace resources and assets

  • Create an Azure Machine Learning workspace
  • Identify Azure Machine Learning resources
  • Identify Azure Machine Learning assets
  • Train models in the workspace

6 - Explore developer tools for workspace interaction

  • Explore the studio
  • Explore the Python SDK
  • Explore the CLI

7 - Make data available in Azure Machine Learning

  • Understand URIs
  • Create a datastore
  • Create a data asset

8 - Work with compute targets in Azure Machine Learning

  • Choose the appropriate compute target
  • Create and use a compute instance
  • Create and use a compute cluster

9 - Work with environments in Azure Machine Learning

  • Understand environments
  • Explore and use curated environments
  • Create and use custom environments

10 - Find the best classification model with Automated Machine Learning

  • Preprocess data and configure featurization
  • Run an Automated Machine Learning experiment
  • Evaluate and compare models

11 - Track model training in Jupyter notebooks with MLflow

  • Configure MLflow for model tracking in notebooks
  • Train and track models in notebooks

12 - Run a training script as a command job in Azure Machine Learning

  • Convert a notebook to a script
  • Run a script as a command job
  • Use parameters in a command job

13 - Track model training with MLflow in jobs

  • Track metrics with MLflow
  • View metrics and evaluate models

14 - Perform hyperparameter tuning with Azure Machine Learning

  • Define a search space
  • Configure a sampling method
  • Configure early termination
  • Use a sweep job for hyperparameter tuning

15 - Run pipelines in Azure Machine Learning

  • Create components
  • Create a pipeline
  • Run a pipeline job

16 - Register an MLflow model in Azure Machine Learning

  • Log models with MLflow
  • Understand the MLflow model format
  • Register an MLflow model

17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

  • Understand Responsible AI
  • Create the Responsible AI dashboard
  • Evaluate the Responsible AI dashboard

18 - Deploy a model to a managed online endpoint

  • Explore managed online endpoints
  • Deploy your MLflow model to a managed online endpoint
  • Deploy a model to a managed online endpoint
  • Test managed online endpoints

19 - Deploy a model to a batch endpoint

  • Understand and create batch endpoints
  • Deploy your MLflow model to a batch endpoint
  • Deploy a custom model to a batch endpoint
  • Invoke and troubleshoot batch endpoints

 

View outline in Word

ADP100

Attend hands-on, instructor-led DP-100: Designing and Implementing a Data Science Solution on Azure training classes at ONLC's nationwide locations. Not near one of our locations? Attend these same live classes from your home/office PC via our Remote Classroom Instruction (RCI) technology.

For additional training options, check out our list of Azure Courses and select the one that's right for you.

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