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

Upcoming Schedules:

• 9th September 2024 - Monday
• 21th October 2024 - Monday
• 11th November 2024 - Monday
• 2nd December 2024 - Monday

Course Overview:

The "Designing and Implementing a Data Science Solution on Azure" course provides participants with the knowledge and skills needed to design and implement data science solutions using Azure services. This course covers various aspects of data science, including data preparation, model training, deployment, and monitoring, preparing participants for the DP-100 certification exam.

Target Audience:

This course is ideal for data scientists, data engineers, and AI professionals who are responsible for designing and implementing data science solutions on Microsoft Azure.

Prerequisites:

Participants should have a fundamental understanding of Azure services, basic knowledge of machine learning, and experience with Python programming. Familiarity with data science concepts is beneficial.

What's Included :

• 4 day instructor-led training
• Official Study guide
• Labs (as required) for hands-on learning
• Certified Trainer delivering the class
• Case studies of implementations
• Hands-on projects & exercises to apply concepts learned throughout the course
• Q&A sessions and troubleshooting exercises

Module 1: Introduction to Azure Data Science

• Overview of data science and Azure Machine Learning
• Introduction to the Azure Machine Learning workspace
• Key concepts and terminologies

Module 2: Data Exploration and Preparation

• Data ingestion and exploration using Azure
• Data preparation and cleaning techniques
• Using Azure Data Factory for data orchestration
• Hands-on lab: Data preparation using Azure Machine Learning and Data Factory

Module 3: Developing Models

• Selecting and training machine learning models
• Using automated machine learning (AutoML) in Azure
• Model evaluation and tuning
• Hands-on lab: Training and evaluating models using Azure Machine Learning

Module 4: Operationalizing Models

• Deploying machine learning models with Azure Machine Learning
• Creating batch and real-time inference pipelines
• Model management and versioning
• Hands-on lab: Deploying and managing models in Azure

Module 5: Monitoring and Maintaining Models

• Monitoring model performance and data drift
• Retraining and updating models
• Implementing MLOps practices for continuous integration and deployment
• Hands-on lab: Monitoring and maintaining deployed models

Module 6: Enhancing Data Science Solutions

• Integrating with Azure Synapse Analytics and Azure Databricks
• Using cognitive services and pre-trained models
• Leveraging big data solutions for data science
• Hands-on lab: Enhancing solutions with advanced Azure services

Module 7: Security and Compliance

• Implementing security best practices for data science solutions
• Managing data privacy and compliance in Azure
• Secure model deployment and access control
• Hands-on lab: Securing data science workflows

Module 8: Case Studies and Best Practices

• Real-world case studies of Azure data science implementations
• Discussing best practices for designing and implementing solutions
• Hands-on lab: Building a complete data science solution on Azure

This course outline provides a detailed learning path for participants to design and implement data science solutions on Azure, ensuring they are well-prepared for the DP-100 certification exam.