Azure Machine Learning (AML)

• An understanding of machine learning concepts.
• Knowledge of cloud computing concepts.
• A general understanding of containers (Docker) and orchestration (Kubernetes).
• Python or R programming experience is helpful.
• Experience working with a command line.

Audience

• Data science engineers
• DevOps engineers interested in machine learning model deployment
• Infrastructure engineers interesting in machine learning model deployment
• Software engineers wishing to automate the integration and deployment of machine learning features with their application

Azure Machine Learning (AML) is a pay-as-you-go, cloud-based environment for training, deploying, automating, managing, and tracking ML models.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment.

By the end of this training, participants will be able to:

• Write highly-accurate machine learning models using Python, R, or zero-code tools.
• Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
• Use Azures interactive workspace to collaboratively develop ML models.
• Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction

Overview of Azure Machine Learning (AML) Features and Architecture

Overview of an End-to-End Workflow in AML (Azure Machine Learning Pipelines)

Provisioning Virtual Machines in the Cloud

Scaling Considerations (CPUs, GPUs, and FPGAs)

Navigating Azure Machine Learning Studio

Preparing Data

Building a Model

Training and Testing a Model

Registering a Trained Model

Building a Model Image

Deploying a Model

Monitoring a Model in Production

Troubleshooting

Summary and Conclusion