Course Description

Course Overview

Designing and Implementing a Data Science Solution on Azure (DP-100) is an advanced training program designed for data scientists and AI professionals to gain expertise in designing and implementing end-to-end data science solutions on Microsoft Azure. Participants will learn how to utilize Azure services and tools to ingest, preprocess, analyze, and visualize data, build machine learning models, and deploy solutions to drive data-driven decision-making within organizations.

Prerequisites

To excel in this course, participants should have a solid understanding of data science concepts, statistics, machine learning algorithms, and programming languages such as Python. Familiarity with Microsoft Azure fundamentals and cloud computing concepts is recommended. Prior experience in data manipulation, visualization, and modeling will be highly beneficial.

Methodology

The course will be delivered through a combination of instructor-led lectures, practical demonstrations, hands-on labs, and interactive discussions. Participants will have access to dedicated Azure environments for hands-on practice, enabling them to apply the concepts learned in real-world scenarios. The course will be led by certified instructors with extensive experience in data science and Azure solutions, ensuring a high-quality learning experience.

Course Outline

• Introduction to Data Science on Azure
• Understanding the role of data science in AI solutions
• Overview of Azure AI services and tools
• Identifying data science challenges and solutions
• Preparing Data for Analysis with Azure
• Ingesting and exploring data using Azure services
• Cleaning and transforming data for analysis
• Implementing data preparation techniques and data stores
• Building and Training Machine Learning Models
• Selecting appropriate machine learning algorithms
• Training and evaluating machine learning models on Azure
• Tuning hyperparameters and improving model performance
• Deploying and Managing Machine Learning Models
• Creating and publishing machine learning pipelines
• Deploying machine learning models as web services
• Monitoring and managing machine learning models in production
• Implementing AI Solutions with Azure
• Integrating machine learning models into applications
• Designing end-to-end AI solutions on Azure
• Implementing natural language processing and computer vision solutions

Outcome

Upon completion of this course, participants will be able to:

• Design and implement end-to-end data science solutions on Microsoft Azure.
• Utilize Azure services for data ingestion, preparation, and exploration.
• Build and train machine learning models using Azure tools and services.
• Deploy machine learning models as web services for real-time applications.
• Implement AI solutions using natural language processing and computer vision.
• Monitor and manage machine learning models in production.

Labs

• Exploring Azure Data Science Environment
• Ingesting and Preparing Data with Azure
• Building and Training Machine Learning Models on Azure
• Deploying Machine Learning Models as Web Services
• Implementing AI Solutions with Azure Cognitive Services