Description

Introduction of Azure Data Scientist Associate

The DP-100: Designing and Implementing a Data Science Solution on Azure certification validates expertise in applying machine learning and data science techniques using Microsoft Azure. This Azure Data Scientist Associate training provides hands-on experience with Azure Machine Learning (Azure ML), model training, deployment, and MLOps to help professionals build and optimize AI-driven solutions.

Prerequisites

• Basic understanding of machine learning and data science concepts
• Familiarity with Python programming (preferred)
• Experience with data analysis and visualization tools
• Knowledge of Azure fundamentals (recommended but not mandatory)

Table of Contents

1. Introduction to DP-100 Certification and Azure AI

1.1 Overview of DP-100 Certification and Exam Objectives
1.2 Understanding the Role of an Azure Data Scientist
1.3 Azure Machine Learning Ecosystem Overview
1.4 Setting Up Azure ML Environment and Workspaces

2. Data Preparation and Feature Engineering

2.1 Importing and Managing Data in Azure ML
2.2 Data Cleaning, Transformation, and Feature Selection
2.3 Feature Engineering Techniques for Model Optimization
2.4 Hands-On: Preparing and Preprocessing Data for ML Models

3. Building Machine Learning Models with Azure ML

3.1 Introduction to Azure Machine Learning Studio
3.2 Selecting and Training Machine Learning Models
3.3 Evaluating Model Performance and Hyperparameter Tuning
3.4 Hands-On: Training and Optimizing ML Models in Azure(Ref: Microsoft Certified: Azure AI Engineer Associate(AI-102))

4. Automating Machine Learning (AutoML) in Azure

4.1 Overview of Azure AutoML Capabilities
4.2 AutoML for Classification, Regression, and Time-Series Forecasting
4.3 Model Interpretability and Explainability in AutoML
4.4 Hands-On: Deploying an Automated Machine Learning Model

5. Model Deployment and Operationalization

5.1 Deploying Models as Web Services using Azure ML
5.2 Integrating ML Models into Applications and APIs
5.3 Implementing Batch and Real-Time Inference
5.4 Hands-On: Deploying an AI Model in Azure Cloud

6. MLOps and Continuous Integration for AI Solutions

6.1 Introduction to MLOps Best Practices
6.2 Implementing Continuous Training and CI/CD for ML Models
6.3 Monitoring Model Performance and Drift Detection
6.4 Hands-On: Automating Model Lifecycle with MLOps

7. Responsible AI and Model Governance

7.1 Principles of Responsible AI and Ethical AI Practices
7.2 Bias Detection and Fairness in Machine Learning Models
7.3 Implementing Model Auditing and Explainability Techniques
7.4 Hands-On: Applying Responsible AI Practices in Azure ML

8. Advanced Machine Learning Workflows in Azure

8.1 Working with Deep Learning Models in Azure
8.2 Leveraging Azure Cognitive Services for AI Enhancements
8.3 Distributed Machine Learning with Azure ML Pipelines
8.4 Hands-On: End-to-End ML Workflow with Azure Services

9. Security and Compliance in Azure ML Solutions

9.1 Managing Access and Security for AI Models
9.2 Implementing Data Protection and Privacy Policies
9.3 Optimizing Cost and Performance of ML Workloads
9.4 Hands-On: Securing an Azure Machine Learning Solution

10. DP-100 Exam Preparation and Practice Tests

10.1 Understanding Exam Format and Question Types
10.2 Best Practices for Exam Success and Time Management
10.3 Sample Case Studies and Practice Questions
10.4 Final Hands-On: Simulated DP-100 Certification Exam Project

Conclusion

This course equips participants with practical skills to design, implement, and deploy AI/ML solutions using Azure Machine Learning. Upon completion, learners will be fully prepared to pass the DP-100 certification exam, enhancing their expertise as certified Azure Data Scientists.

Reference