MLOps Engineering on AWS

Enroll for 3-day MLOps Engineering on AWS from Koenig Solutions accredited by AWS.  In this course learn the importance of data, model, and code to successful ML deployments. This MLOps Engineering on AWS course builds upon, and extends the DevOps practice prevalent in software development to building, training, and deploying ML models.

Through a blend of hands-on labs and interactive lectures, you will learn the use of tools, automation, processes, and teamwork in addressing the challenges associated with hand-offs between data engineers, data scientists, software developers, and operations. Also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators.

Target Audience:

This curriculum is intended for:

• DevOps engineers
• ML engineers
• Developers/Operations with responsibility for operationalizing ML models.

Learning Objectives:

After completing this course, you will be able to:

• Describe Machine Learning Operations
• Understand the key differences between DevOps and MLOps
• Describe the machine learning workflow
• Discuss the importance of communications in MLOps
• Explain end-to-end options for automation of ML workflows
• List key Amazon SageMaker features for MLOps automation
• Build an automated ML process that builds, trains, tests and deploys models
• Build an automated ML process that retrains the model based on change(s) to the model code
• Maximize confidence in the ML release process
• Deployment operations
• Identify potential security threats in ML and explain basic mitigation approaches
• Describe why monitoring is important
• Detect data drifts in the underlying input data
• Demonstrate how to monitor ML models for bias
• Explain how to monitor model resource consumption and latency
• Discuss how to integrate human-in-the-loop reviews of model results in production.