DP-100T01: Designing and Implementing a Data Science Solution on Azure

Last Updated: 08-07-2025

The DP-100T01: Designing and Implementing a Data Science Solution on Azure course is designed for data professionals who want to develop advanced data science skills and implement machine learning solutions in the Azure cloud environment. This course provides comprehensive training on designing, developing, and deploying end-to-end data science solutions using Azure Machine Learning, Azure Databricks, and other Azure tools. You will learn how to prepare data, train models, manage experiments, optimize machine learning workflows, and deploy models for production use. Whether you're a data scientist, ML engineer, or cloud professional, this course will help you develop the skills to solve real-world business problems with data science in Microsoft Azure. This course also prepares you for the DP-100 certification exam, validating your expertise in designing and implementing data science solutions on Azure.

Schedule & Fee
Learning Objectives
Prerequisites
Target Audience
Course Modules
FAQs

Schedule	Learners	Course Fee (Incl. of all Taxes)	Register Your Interest
October 13^th - 16^th 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		40% Off ₹69,472 ₹41,683 Fast Filling! Hurry Up.
October 18^th - 26^th 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		40% Off ₹69,472 ₹41,683
October 20^th - 29^th 06:00 PM - 10:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		40% Off ₹69,472 ₹41,683
October 27^th - 30^th 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		40% Off ₹69,472 ₹41,683
November 01^st - 09^th 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		50% Off ₹69,472 ₹34,736
November 03^rd - 06^th 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		50% Off ₹69,472 ₹34,736
November 10^th - 13^th 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		50% Off ₹69,472 ₹34,736
November 15^th - 23^rd 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		50% Off ₹69,472 ₹34,736
November 17^th - 26^th 06:00 PM - 10:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		50% Off ₹69,472 ₹34,736
November 24^th - 27^th 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		50% Off ₹69,472 ₹34,736
November 29^th - 07^th 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		50% Off ₹69,472 ₹34,736
December 01^st - 04^th 09:00 AM - 05:00 PM (IST) Live Virtual Classroom (Duration : 32 Hours)		60% Off ₹69,472 ₹27,789

Course Prerequisites

Familiarity with basic data science concepts, including statistics, machine learning, and data analysis.
Experience with programming languages such as Python and R for data analysis and model development.
A basic understanding of Azure services and cloud computing concepts is recommended, though not mandatory.
Familiarity with data manipulation and analysis tools (e.g., Pandas, NumPy, scikit-learn) will be beneficial.
Experience with databases and SQL will be helpful for data wrangling and preparation tasks.

Learning Objectives

By the end of the DP-100T01: Designing and Implementing a Data Science Solution on Azure course, you will be able to:

Understand the architecture and components of Azure Machine Learning and how it integrates with other Azure services like Azure Databricks and Azure Storage.
Design and implement data science solutions that involve the use of machine learning models and frameworks in Azure.
Prepare data for machine learning tasks by cleaning, transforming, and exploring datasets using Azure Machine Learning and Azure Databricks.
Train and evaluate machine learning models, including regression, classification, and clustering models, using Azure Machine Learning.
Implement model management strategies such as versioning, model selection, and hyperparameter tuning in Azure.
Use Azure Machine Learning Pipelines to automate and scale machine learning workflows for deployment and training.
Deploy machine learning models as web services or integrate them into real-world applications using Azure Kubernetes Service (AKS) and Azure Functions.
Monitor and maintain deployed machine learning models in production, ensuring they continue to deliver accurate predictions.
Ensure the proper governance of data and models using Azure Machine Learning to maintain security, compliance, and privacy standards.
Prepare for the DP-100 certification exam, gaining practical experience with hands-on labs and real-world scenarios.

Target Audience

This course is ideal for:

Data scientists and machine learning engineers who want to build expertise in developing and deploying AI and machine learning solutions on Azure.
Cloud professionals who aim to specialize in data science and AI solutions within the Azure ecosystem.
ML developers looking to learn how to design, implement, and scale data science models using Azure Machine Learning and Azure Databricks.
Business analysts or IT professionals wanting to transition into the field of data science and machine learning on Azure.
Professionals preparing for the DP-100 certification exam to validate their proficiency in designing and implementing data science solutions using Azure Machine Learning and other related Azure tools.

Course Modules

Course Introduction

Course Introduction

01: Design a Machine Learning Solution

Design a machine learning model training solution

02: Explore and Configure the Azure Machine Learning Workspace

Explore the Azure Machine Learning workspace resources and assets
Explore developer tools for workspace interaction
Make data available in Azure Machine Learning
Work with compute targets in Azure Machine Learning
Work with environments in Azure Machine Learning

03: Experiment with Azure Machine Learning

Explore Automated Machine Learning
Find the best classification model with Automated Machine Learning
Track model training in notebooks with MLflow

04: Optimize Model Training with Azure Machine Learning

Run a training script as a command job in Azure Machine Learning
Track model training with MLflow in jobs

05: Manage and Evaluate Models with Azure Machine Learning

Register an MLflow model in Azure Machine Learning
Create and explore the Responsible AI dashboard

06: Deploy and Consume Models with Azure Machine Learning

Deploy a model to a managed online endpoint
Deploy a model to a batch endpoint

07: Optimize Language Models for Generative AI Applications

Explore and deploy models from the model catalog in Azure AI Foundry
Get started with prompt flow in Azure AI Foundry
Build a RAG-based agent with your own data using Azure AI Foundry
Fine-tune a language model with Azure AI Foundry
Evaluate the performance of generative AI apps with Azure AI Foundry

Course FAQs

The DP-100 certification, also known as Microsoft Certified: Azure Data Scientist Associate, validates your expertise in designing and implementing data science solutions using Microsoft Azure. Earning this certification can significantly boost your career by demonstrating your ability to apply machine learning techniques, analyze data, and optimize data science models on the Azure platform. It opens doors to roles like Data Scientist, Machine Learning Engineer, and AI Specialist.

The DP-100 course provides hands-on experience in working with Azure machine learning services. You'll learn how to design, implement, and deploy machine learning models, manage data, and use Azure tools for model training, evaluation, and optimization. The training also focuses on best practices for building scalable, repeatable, and efficient data science solutions on Azure.

With the DP-100 certification, you can pursue various roles in the field of data science and artificial intelligence, such as: Data Scientist Machine Learning Engineer AI Solutions Architect Cloud Data Scientist Data Analyst with AI/ML expertise The certification demonstrates your proficiency in implementing end-to-end data science solutions in the Azure environment, making you an in-demand professional for organizations embracing AI and machine learning.

The DP-100 certification helps businesses by providing skilled professionals who can design and implement machine learning models and AI solutions using Azure. These experts can create data science workflows that help organizations make data-driven decisions, optimize operations, and develop innovative products and services using AI technologies.

The DP-100 course will provide you with hands-on skills in: Designing machine learning workflows using Azure Machine Learning Studio Implementing data preparation and feature engineering techniques Building, training, and evaluating machine learning models Optimizing models for performance and scalability Deploying and managing models in production Monitoring model performance and retraining models as necessary Ensuring data privacy and security while working with sensitive information These skills are directly applicable to real-world data science projects and are critical for successfully implementing machine learning solutions in the cloud.

Yes, earning the DP-100 certification will enhance your job prospects significantly. As organizations continue to adopt AI and machine learning technologies, there is an increasing demand for data scientists who can implement machine learning models using cloud platforms like Azure. This certification will make you a highly competitive candidate for roles in the growing field of data science and AI.

The DP-100 course is ideal for individuals with some prior experience in data science, machine learning, or programming. While it’s not designed for complete beginners, it is well-suited for professionals who are familiar with data analysis or have a background in programming languages like Python and R. If you are new to Azure, starting with AZ-900 (Azure Fundamentals) can help build a foundational understanding of the platform.

Data preprocessing and feature engineering are critical steps in the data science workflow. The DP-100 course teaches how to clean and prepare data for machine learning, extract valuable features, handle missing values, and transform data to make it suitable for modeling. This ensures that machine learning models are built on high-quality, meaningful data, leading to more accurate predictions.

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