Azure Machine Learning (AML) Training is a comprehensive, hands-on program designed to equip learners with the skills required to build, train, deploy, and manage machine learning models using Microsoft Azure Machine Learning services. This training focuses on ML workflows, data preparation, model training, automated machine learning (AutoML), MLOps practices, model deployment, and monitoring in cloud environments. Participants will gain real-world experience in developing end-to-end machine learning solutions on Azure.

Participants will gain strong practical expertise in Azure Machine Learning, enabling them to build scalable ML solutions, deploy models in production, and implement MLOps workflows in cloud environments.

• Data Scientists and Machine Learning Engineers
• AI Developers and Data Analysts
• Cloud Engineers working on AI/ML solutions
• Software Developers transitioning into machine learning
• IT Professionals interested in AI and MLOps

• Basic understanding of Python programming
• Familiarity with machine learning concepts and statistics
• Basic knowledge of Azure cloud services is helpful
• Understanding of data processing and analytics fundamentals

• Azure Machine Learning workspace setup and configuration
• Data preparation and feature engineering techniques
• Model training using Azure ML studio and SDK
• Automated Machine Learning (AutoML) implementation
• Model evaluation and hyperparameter tuning
• Model deployment using endpoints and APIs
• MLOps workflow implementation and monitoring

Upon completion of this training, participants will be able to design, build, and deploy machine learning models using Azure Machine Learning. They will be capable of managing the full ML lifecycle, implementing AutoML solutions, and deploying production-ready AI systems.

Introduction to Azure Machine Learning
• Overview of Azure ML ecosystem
• Azure ML workspace and components
• Data ingestion and dataset management
• Introduction to ML lifecycle

Data Preparation and Feature Engineering
• Data cleaning and transformation techniques
• Feature selection and engineering basics
• Using Azure ML data assets
• Exploratory data analysis (EDA)
Model Development and Training
• Training models using Azure ML studio
• Using Python SDK for ML workflows
• Model evaluation metrics
• Hyperparameter tuning techniques

Automated Machine Learning (AutoML)
• Introduction to AutoML concepts
• Running AutoML experiments
• Comparing and selecting best models
• Model interpretation basics
Model Deployment and MLOps
• Deploying models as real-time endpoints
• Batch inference concepts
• Model versioning and management
• CI/CD for machine learning pipelines

Monitoring and Production Best Practices
• Model monitoring and drift detection
• Logging and performance tracking
• Security and governance in ML systems
• Best practices for scalable ML deployment

• Azure ML workspace and lifecycle
• Data preparation and feature engineering
• Model training and evaluation
• AutoML and experimentation
• Model deployment and APIs
• MLOps and monitoring practices

• Hands-on model building and training exercises
• AutoML experiment assignments
• Model deployment and API creation tasks
• Mini project on end-to-end ML pipeline

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.
 

Participants who successfully complete the training will receive an AcadNXT Certification in Azure Machine Learning (AML) Training, validating their expertise in machine learning lifecycle management, model development, deployment, and MLOps using Microsoft Azure.