Machine Learning models with Azure Machine Learning training course

Introduction to Machine Learning Concepts

• Understand core machine learning principles and terminology

• Explore common ML workflows and model types

• Learn how models are trained, evaluated, and deployed

Exploring Azure Machine Learning

• Navigate the Azure Machine Learning workspace

• Understand compute targets, environments, and resources

• Learn how Azure ML supports the full ML lifecycle

Training Machine Learning Models

• Build and train models using Azure Machine Learning

• Work with datasets, features, and labels

• Evaluate model performance and accuracy

Using MLflow and AutoML

• Track experiments, metrics, and model versions with MLflow

• Automate model selection and training with AutoML

• Manage model lifecycle and reproducibility

Working with Compute Targets and Environments

• Configure and use compute clusters for training

• Manage environments and dependencies for consistent results

• Optimise resources for efficient model training

Hyperparameter Tuning

• Perform hyperparameter optimisation to improve model performance

• Use automated and manual tuning techniques

• Evaluate results and select the best model configuration

Running Pipelines in Azure Machine Learning

• Design and execute machine learning pipelines

• Automate workflows for data preparation, training, and evaluation

• Monitor pipeline execution and results

Deploying Models to Online and Batch Endpoints

• Deploy trained models to a managed online endpoint for real-time scoring

• Deploy models to a batch endpoint for large-scale predictions

• Monitor and manage deployed models