DP-100: Designing and Implementing a Data Science Solution on Azure is an intermediate-level course designed to help learners build, train, deploy, and manage machine learning solutions using Microsoft Azure. The course focuses on the complete data science lifecycle, combining data preparation, machine learning concepts, and Azure-based tools to deliver production-ready AI solutions.

Learners begin by exploring data science fundamentals and how Azure supports data-driven workflows. The course then covers data exploration and preparation using services such as Azure Data Factory and Azure Databricks, followed by building and evaluating machine learning models using Azure Machine Learning Studio. Participants learn how to select algorithms, tune models, and evaluate performance using industry-standard metrics.

The training also emphasizes deploying and managing AI solutions on Azure. Learners gain exposure to model deployment, scalable infrastructure using Azure Kubernetes Service (AKS), and best practices for model versioning and lifecycle management. In addition, the course introduces Azure Cognitive Services, enabling learners to understand how pre-built AI capabilities such as computer vision, text analytics, and speech services can be integrated into applications.

By the end of the course, learners will have a strong understanding of how to design and implement end-to-end data science solutions on Azure and will be well prepared for the DP-100 certification and real-world data science and machine learning roles on the Azure platform.

Module 1: Introduction to Data Science and Azure

• Data science concepts and methodologies

• Data science lifecycle and workflows

• Role of Microsoft Azure in data science solutions

• Azure services used in data science

• Setting up an Azure account

• Navigating the Azure Portal

Module 2: Data Exploration and Preparation

• Types of data sources and data formats

• Data exploration techniques

• Data cleaning methods:

• Handling missing values

• Identifying outliers

• Removing duplicates

• Data ingestion using Azure Data Factory

• Data transformation and pipelines

• Introduction to Azure Databricks for collaborative data processing

Module 3: Fundamentals of Machine Learning

• What is machine learning

• Types of machine learning:

• Supervised learning

• Unsupervised learning

• Machine learning lifecycle

• Common algorithms:

• Regression

• Classification

• Clustering

• Preparing data for machine learning on Azure

Module 4: Building Machine Learning Models on Azure

• Introduction to Azure Machine Learning Studio

• Creating and managing ML workspaces

• Data splitting:

• Training datasets

• Validation datasets

• Test datasets

• Model training workflows

• Model evaluation metrics

• Hyperparameter tuning techniques

Module 5: Deploying and Managing AI Solutions

• Overview of model deployment on Azure

• Creating and deploying web services for ML models

• Scalable deployments using Azure Kubernetes Service (AKS)

• Model versioning and lifecycle management

• Monitoring deployed models and performance

Module 6: Leveraging Azure Cognitive Services

• Overview of Azure Cognitive Services

• Computer Vision services for image analysis

• Text Analytics:

• Sentiment analysis

• Language detection

• Speech services:

• Speech-to-text

• Text-to-speech

• Integrating Cognitive Services into applications

Module 7: End-to-End Data Science Solution Design

• Designing end-to-end ML solutions on Azure

• Selecting appropriate Azure services for use cases

• Combining data pipelines, ML models, and deployment

• Security and governance considerations for ML solutions

• Best practices for production-ready AI system

Module 8: DP-100 Exam Preparation and Revew

• Mapping course content to DP-100 exam objectives

• Review of key concepts and services

• Sample exam-style questions

• Certification tips and exam strategy

• Guidance on next steps in the Azure data science learning path

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

• Basic understanding of data concepts and analytics fundamentals

• Working knowledge of Python for data analysis

• Familiarity with machine learning concepts such as regression and classification

• Basic experience with Microsoft Azure fundamentals

• Comfort working 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 on Microsoft Azure

• Explore, clean, and prepare data using Azure Data Factory and Azure Databricks

• Select appropriate machine learning algorithms for different problem types

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

• Apply model evaluation metrics and interpret performance results

• Perform hyperparameter tuning to improve model accuracy and reliability

• Deploy machine learning models as web services on Azure

• Implement scalable deployments using Azure Kubernetes Service (AKS)

• Manage model versioning, monitoring, and lifecycle best practices

• Integrate Azure Cognitive Services for vision, text, and speech use cases

• Design production-ready data science solutions on Azure

• Demonstrate readiness for the DP-100 certification and real-world Azure data science roles

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

• Data Scientist

• Machine Learning Engineer

• Azure Data Scientist Associate

• Applied Machine Learning Engineer

• AI Engineer (Azure-focused)

• Data Science Consultant

• Cloud Data Scientist

• ML Solutions Engineer

• Data Scientist – Cloud Platforms

• AI & Machine Learning Specialist