DP-100: Designing and Implementing a Data Science Solution

Instructor: Dominic Thevan

This course focuses on building and deploying machine learning models using Azure Machine Learning for enterprise data science solutions.

CA$1,600.00

CA$2,400.00

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At A Glance

This course provides comprehensive training in designing and implementing data science solutions using Azure Machine Learning, covering the complete ML lifecycle from data preparation to model deployment. Ideal for data scientists, ML engineers, or developers working with machine learning solutions, it offers hands-on experience with Azure ML Studio, automated ML, and MLOps practices. Participants will gain the expertise needed to build scalable ML solutions and prepare for the DP-100 certification exam.

Difficulty

Some related experience required

Duration

20 sessions

(40 hours)

Overview

Master the art of data science with the DP-100 course, your comprehensive guide to building production-ready machine learning solutions on Azure. This advanced program covers the complete ML lifecycle, including data engineering, model development, and deployment strategies, updated for 2025 to include the latest Azure Machine Learning features, responsible AI practices, and MLOps capabilities. Perfect for data scientists seeking the Azure Data Scientist Associate certification, enabling you to deliver enterprise-scale ML solutions that drive business value.

With 40+ hours of intensive hands-on labs, real-world datasets, and advanced projects, you’ll learn from certified data scientists through practical demonstrations and algorithmic guidance. This premium training includes exclusive notebooks, deployment templates, and community access, providing exceptional value for mastering ML at scale. Thousands of data scientists have built world-class models using skills from this course – join them and architect the future of intelligent applications today!

The Syllabus

Discover the key topics covered in this course:

Module 1: Design a machine learning solution

You would learn about:

  • Define business requirements and success metrics for ML projects
  • Design data ingestion and preparation strategies
  • Select appropriate algorithms and modeling approaches
  • Plan compute resources and scaling requirements
  • Understand ethical AI and responsible ML practices
Module 2: Explore and configure the Azure Machine Learning workspace

You would learn about:

  • Set up Azure ML workspace and compute instances
  • Configure datastores and dataset management
  • Implement security and access control policies
  • Learn about workspace organization and resource management
  • Describe integration with Azure services and third-party tools
Module 3: Experiment and train models

You would learn about:

  • Develop experiments using Azure ML SDK and notebooks
  • Implement automated machine learning for rapid prototyping
  • Configure custom training scripts and environments
  • Learn about hyperparameter tuning and model optimization
  • Describe distributed training and parallel processing
Module 4: Optimize and manage models

You would learn about:

  • Evaluate model performance using multiple metrics
  • Implement model interpretation and explainability techniques
  • Configure model versioning and lifecycle management
  • Learn about model drift detection and retraining strategies
  • Describe A/B testing and champion-challenger patterns
Module 5: Deploy and consume models

You would learn about:

  • Deploy models as real-time and batch inference endpoints
  • Configure containerization and orchestration for model serving
  • Implement authentication and security for model APIs
  • Learn about monitoring and logging for production models
  • Describe scaling strategies and performance optimization
Module 6: Implement MLOps practices

You would learn about:

  • Build CI/CD pipelines for machine learning workflows
  • Configure automated testing and validation for ML models
  • Implement Infrastructure as Code for ML environments
  • Learn about governance and compliance for ML operations
  • Describe collaboration patterns and team development practices
Module 7: Final Review and Projects

You would learn about:

  • Review key data science concepts with end-to-end exercises
  • Complete comprehensive ML solution development projects
  • Prepare for the DP-100 certification exam with practical scenarios
  • Receive expert feedback on model architecture and MLOps practices
  • Collaborate on enterprise data science case studies

Meet The Graduated Learner

Don’t just take my word for it, check out what existing students have to say about the course:

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DP-100: Designing and Implementing a Data Science Solution

CA$1,600.00

CA$2,400.00

Sale!

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