DP-100: Designing and Implementing a Data Science Solution on Azure is a comprehensive, hands-on training program designed to help learners build, deploy, and manage end-to-end data science and machine learning solutions using Microsoft Azure.

This course introduces learners to core data science concepts and progressively moves into practical implementation using Azure’s enterprise-grade services such as Azure Machine Learning, Azure Databricks, Azure Data Factory, Azure Kubernetes Service (AKS), and Azure Cognitive Services. Participants learn how to explore and prepare data, select and train machine learning models, evaluate performance, tune models for optimal results, and deploy scalable AI solutions in real-world environments.

Through guided instruction, practical labs, and assignments, learners gain experience working with supervised and unsupervised learning techniques, model lifecycle management, and AI deployment best practices on Azure. The course emphasizes real-world scenarios and industry-aligned workflows, ensuring learners understand not just how to build models, but also how to operationalize them effectively.

This training is aligned with the Microsoft DP-100 certification, making it ideal for professionals looking to strengthen their data science skills on Azure, transition into cloud-based AI roles, or implement machine learning solutions within enterprise environments.

To successfully complete DP-100: Designing and Implementing a Data Science Solution on Azure, learners should have:

• Basic understanding of Python programming for data analysis

• Familiarity with data concepts such as datasets, tables, and basic data cleaning

• Introductory knowledge of machine learning fundamentals (helpful but not mandatory)

• Basic awareness of cloud computing concepts

• Willingness to work with hands-on labs, assignments, and real-world datasets

By the end of this course, you will be able to:

• Understand the end-to-end data science lifecycle and how it is implemented on Microsoft Azure

• Explore, prepare, and transform data using Azure Data Factory, Databricks, and Azure Machine Learning

• Apply machine learning concepts including supervised and unsupervised learning to real-world datasets

• Build, train, and evaluate machine learning models using Azure Machine Learning Studio

• Select appropriate machine learning algorithms for regression, classification, and clustering problems

• Tune models using hyperparameter optimization techniques to improve performance

• Deploy machine learning models as scalable web services using Azure infrastructure and AKS

• Monitor, manage, and version deployed models throughout their lifecycle

• Leverage Azure Cognitive Services for vision, text, and speech-based AI solutions

• Implement best practices for model governance, reliability, and scalability

• Design and implement end-to-end data science solutions on Azure aligned with enterprise requirements

• Prepare for real-world scenarios and DP-100 certification–aligned tasks

This course prepares learners for roles focused on designing, building, and deploying data science and machine learning solutions on Microsoft Azure. After completing the training, learners will be better prepared for positions such as:

• Data Scientist (Azure)

• Machine Learning Engineer

• Azure Data Scientist

• Applied Machine Learning Engineer

• AI Engineer (Azure)

• Cloud Data Scientist

• Data Science Engineer

• Machine Learning Solutions Engineer

• Data Scientist – Cloud & AI Platforms

• Analytics Engineer (Machine Learning)

Module 1: Introduction to Data Science and Azure

• Overview of data science concepts, lifecycle, and methodologies

• Role of data science in modern business and AI-driven decision-making

• Introduction to Microsoft Azure and its data science ecosystem

• Understanding Azure services relevant to data science and machine learning

• Setting up an Azure account and navigating the Azure Portal

Module 2: Azure Environment Setup for Data Science

• Azure resource groups and workspace organization

• Creating and managing Azure Machine Learning workspaces

• Understanding compute targets and environments

• Managing datasets and data assets in Azure ML

• Best practices for cloud-based data science projects

Module 3: Data Sources, Exploration, and Understanding

• Types of data sources: structured, semi-structured, and unstructured

• Connecting to data from databases, files, and cloud storage

• Exploratory Data Analysis (EDA) concepts and techniques

• Identifying data quality issues and patterns

• Preparing datasets for machine learning workflows

Module 4: Data Preparation and Transformation

• Data cleaning techniques: handling missing values, duplicates, and outliers

• Feature selection and basic feature engineering concepts

• Using Azure Data Factory for data ingestion and transformation

• Introduction to Azure Databricks for collaborative data processing

• Building repeatable and scalable data preparation pipelines

Module 5: Fundamentals of Machine Learning

• Introduction to machine learning and its business applications

• Machine learning types: supervised, unsupervised, and semi-supervised

• Understanding the machine learning lifecycle

• Overview of common algorithms:

• Regression

• Classification

• Clustering

• Mapping machine learning concepts to Azure services

Module 6: Model Development Using Azure Machine Learning

• Introduction to Azure Machine Learning Studio

• Creating experiments and pipelines

• Data splitting strategies: training, validation, and test datasets

• Training machine learning models using Azure ML

• Understanding model outputs and evaluation results

Module 7: Model Evaluation and Optimization

• Model evaluation metrics for regression and classification

• Comparing multiple models and experiments

• Hyperparameter tuning techniques

• Improving model accuracy and performance

• Tracking experiments and results in Azure ML

Module 8: Deploying Machine Learning Models

• Introduction to model deployment concepts

• Deploying models as web services in Azure

• Understanding Azure Kubernetes Service (AKS) for scalable

  deployments

• Managing endpoints and inference workflows

• Best practices for model versioning and lifecycle management

Module 9: Operationalizing and Managing AI Solutions

• Monitoring deployed machine learning models

• Managing model updates and retraining

• Ensuring scalability, reliability, and security

• Governance considerations for AI solutions

• Introduction to MLOps concepts on Azure

Module 10: Leveraging Azure Cognitive Services

• Overview of Azure Cognitive Services and real-world use cases

• Implementing Computer Vision for image analysis

• Using Text Analytics for sentiment analysis and language detection

• Integrating Speech Services for voice recognition applications

• Combining cognitive services with machine learning solutions

Module 11: End-to-End Azure Data Science Workflow

• Designing an end-to-end data science solution on Azure

• Integrating data ingestion, model training, deployment, and

• consumption

• Applying best practices learned throughout the course

• Understanding real-world enterprise data science architectures

• Preparing for DP-100 certification scenarios

Module 12: Assignments and Hands-On Practice

• Guided hands-on labs aligned with each module

• Real-world datasets and scenarios

• End-to-end practical exercises

• Reinforcement of concepts through assignments

• Review and discussion of solutions