Overview
With DevOps-style techniques, machine learning models can now be built, trained, and deployed more effectively. Enroll in the AWS MLOps Engineering course.
The DevOps methodologies used in software development are expanded upon and applied to the process of generating machine learning models in the MLOps Engineering on AWS course. The course places a strong emphasis on the necessity of code, models, and data for effective ML implementations. It will show how to overcome the difficulties posed by hand-offs between data scientists, software developers, operations, and engineers by utilizing tools, automation, procedures, and cooperation.
You will also learn about using tools and procedures to keep an eye on and take appropriate action when the model prediction in production begins to deviate from predetermined key performance metrics in this MLOps Engineering on AWS class.
Note: Lab time is only accessible during class; it cannot be used after that. There are extra lab fees for "repeat students."
- 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
- DevOps Engineer
- Data Scientist
- Software Developer
- Cloud Developer
Required
AWS Technical Essentials
Practical Data Science with Amazon SageMaker
DevOps Engineering on AWS
Recommended
The Elements of Data Science (digital course), or equivalent experience
Machine Learning Terminology and Process (digital course)