What’s Included?
High-Quality Video, E-book & Audiobook
Module Quizzes
AI Mentor
Access for Tablet & Phone
Prerequisites
- Proficiency in Python programming
- Experience with machine learning frameworks (e.g., Scikit-Learn, PyTorch, TensorFlow)
- Familiarity with cloud computing concepts
- Basic understanding of data science workflows
Skills You’ll Gain
- Model Building Workflow
- Azure ML Training
- Scalable Model Deployment
- MLflow Experiment Tracking
- Performance Monitoring Tools
- Model Management Strategy
- Cloud ML Automation
- Workflow Orchestration Setup
Self Study Materials Included
Videos
Engaging visual content to enhance understanding and learning experience.
Podcasts
Insightful audio sessions featuring expert discussions and real-world cases.
Audiobooks
Listen and learn anytime with convenient audio-based knowledge sharing.
E-Books
Comprehensive digital guides offering in-depth knowledge and learning support.
Module Wise Quizzes
Interactive assessments to reinforce learning and test conceptual clarity.
Additional Resources
Supplementary references and list of tools to deepen knowledge and practical application.
Tools You’ll Master
MLflow
Azure CLI & SDKs
Azure Notebooks
Azure Monitor
What You’ll Learn
Manage ML Workspaces
Configure and organize Azure Machine Learning environments.
Train & Optimize Models
Use cloud resources to improve model performance.
Deploy & Monitor Models
Publish models via endpoints and track performance metrics.
Versioning & Retraining
Manage model versions and retrain based on new data.
Course Modules
Lesson 1: Explore and Configure the Azure Machine Learning Workspace
Module 1: Explore Azure Machine Learning Workspace Resources and Assets
Module 2: Explore Developer Tools for Workspace Interaction
Module 3: Make Data Available in Azure Machine Learning
Module 4: Work with Compute Targets in Azure Machine Learning
Module 5: Work with Environments in Azure Machine Learning
Lesson 2: Experiment with Azure Machine Learning
Module 1: Find the Best Classification Model with Automated Machine Learning
Module 2: Track Model Training in Jupyter Notebooks with MLflow
Lesson 3: Optimize Model Training with Azure Machine Learning
Module 1: Run a Training Script as a Command Job in Azure Machine Learning
Module 2: Track Model Training with MLflow in Jobs
Module 3: Perform Hyperparameter Tuning with Azure Machine Learning
Module 4: Run Pipelines in Azure Machine Learning
Lesson 4: Manage and Review Models in Azure Machine Learning
Module 1: Register an MLflow Model in Azure Machine Learning
Module 2: Create and Explore the Responsible AI Dashboard for a Model in Azure Machine Learning
Lesson 5: Deploy and Consume Models with Azure Machine Learning
Module 1: Deploy a Model to a Managed Online Endpoint
Module 2: Deploy a Model to a Batch Endpoint
Lesson 6: Develop Generative AI Apps in Azure
Module 1: Plan and Prepare to Develop AI Solutions on Azure
Module 2: Choose and Deploy Models from the Model Catalog in Azure AI Foundry Portal
Module 3: Develop an AI App with the Azure AI Foundry SDK
Module 4: Get Started with Prompt Flow to Develop Language Model Apps in the Azure AI Foundry
Module 5: Develop a RAG-Based Solution with Your Own Data Using Azure AI Foundry
Module 6: Fine-Tune a Language Model with Azure AI Foundry
Module 7: Implement a Responsible Generative AI Solution in Azure AI Foundry
Module 8: Evaluate Generative AI Performance in Azure AI Foundry Portal
Frequently Asked Questions
No, it’s intended for those with prior ML and Python experience.
It supports the Azure Data Scientist Associate certification.
Yes, MLflow is integrated for experiment tracking and management.
Yes, labs are based on real-world Azure ML scenarios.
Yes, both instructor-led and self-paced formats are available.
