Azure Machine Learning Service Course

This Azure Machine Learning Service course is designed for beginners and intermediate learners who want to build, train, and deploy machine learning and deep learning models using Microsoft Azure’s cloud-based platform. The course provides a complete hands-on learning experience that covers the full machine learning lifecycle, starting from data preparation and workspace setup to model training, evaluation, deployment, and production management.

Learners gain practical experience using both no-code and low-code tools such as AutoML and Azure ML Designer, as well as exposure to deep learning workflows using FastAI inside Azure ML.

1.1 Introduction to Azure Machine Learning Service

This section introduces Azure Machine Learning Service and explains its role as a complete cloud-based platform for building and managing machine learning solutions. Learners understand how Azure ML Service supports data scientists and engineers by providing tools for data preparation, model training, deployment, and monitoring.

It also explains how Azure ML integrates different development approaches, including code-based workflows, automated machine learning, and visual pipeline design.

1.1.1 Overview of Azure Machine Learning Workspace

This part explains how the Azure ML workspace acts as the central environment where all machine learning assets such as datasets, experiments, models, and compute resources are managed and organized.

1.1.2 Setting Up Azure ML Environment

Here learners explore how to create and configure an Azure Machine Learning environment, including setting up subscriptions, resource groups, and workspace configurations needed for ML development.

1.2 Data Preparation and Dataset Management

This section focuses on how to prepare and manage datasets effectively within Azure Machine Learning Service. Learners understand how data quality directly impacts model performance and accuracy.

It also introduces best practices for structuring datasets for machine learning experiments.

1.2.1 Organizing and Uploading Datasets

This topic explains how to upload, store, and organize datasets inside Azure ML workspace for efficient access during training and experimentation.

1.2.2 Data Cleaning and Preprocessing Techniques

Here learners explore how to clean raw data by handling missing values, removing inconsistencies, and preparing features for machine learning models.

1.3 Automated Machine Learning (AutoML)

This section introduces Azure’s Automated Machine Learning feature, which allows users to automatically build, train, and optimize machine learning models without writing code.

It helps learners understand how automation can speed up model development and improve performance through intelligent algorithm selection.

1.3.1 Running AutoML Experiments

This part explains how to configure and run AutoML experiments inside Azure ML, including selecting target variables, defining metrics, and choosing model types.

1.3.2 Evaluating AutoML Results

Here learners learn how to analyze AutoML outputs, compare model performance, and select the best-performing model for deployment.

1.4 Azure Machine Learning Designer

This section focuses on the visual, drag-and-drop interface of Azure ML Designer, which allows users to build machine learning pipelines without coding.

Learners understand how workflows are structured and executed visually in cloud environments.

1.4.1 Building Visual Machine Learning Pipelines

This topic explains how to create machine learning pipelines using visual modules such as data input, transformation, model training, and evaluation components.

1.4.2 Understanding Workflow Design in Designer

Here learners explore how different components interact within a pipeline and how data flows from input to output in a structured machine learning process.

1.5 Deep Learning with FastAI in Azure ML

This section introduces deep learning workflows using FastAI within Azure Machine Learning Service. Learners gain exposure to neural network training in a cloud environment.

It helps bridge the gap between traditional machine learning and deep learning applications.

1.5.1 Training Deep Learning Models with FastAI

This part explains how to train neural networks using FastAI frameworks integrated into Azure ML environments.

1.5.2 Understanding Cloud-Based Deep Learning Workflows

Here learners understand how deep learning models are executed, optimized, and managed within Azure’s cloud infrastructure.

1.6 Model Management and Deployment

This section focuses on the final stages of the machine learning lifecycle, including model registration, versioning, and deployment into production environments.

1.6.1 Registering and Managing Models

This topic explains how to store trained models in Azure ML registry and manage different versions for reproducibility and tracking.

1.6.2 Deploying Machine Learning Models

Here learners explore how to deploy models as real-time endpoints or batch services for production use.

1.7 Final Skills and Learning Outcomes

By the end of this course, learners will be able to confidently build, train, deploy, and manage machine learning and deep learning solutions using Azure Machine Learning Service.

They will also understand how to use AutoML, Designer, and FastAI to create end-to-end AI workflows suitable for real-world production environments.