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Python ProgrammingMLOps Engineering on AWS
Productionize ML models using repeatable and reliable workflows with MLOps Engineering on AWS
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Course : MLOps Engineering on AWS
Level : Intermediate
Type : Live online or Classroom
Length :3 days
MLOps Engineering on AWS will help you bring DevOps-style practices into the building, training, and deployment of ML models by learning from an expert AWS instructor.
By the end of the course, go from learning to doing by building and troubleshooting an ML pipeline.
MLOps engineers who want to productionize and monitor ML models in the AWS cloud
DevOps engineers who will be responsible for successfully deploying and maintaining ML models in production
DevOps Engineering on AWS course, or equivalent experience
Knowledge of Amazon SageMaker or equivalent experience
Explain the benefits of MLOps
Compare and contrast DevOps and MLOps
Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies
Set up experimentation for MLOps with Amazon SageMaker
And much more
Instructor-led Virtual online live Training.
Weekend Classes
Evening Classes
Weekdays Classes
Course Outline
Module 1: Introduction to MLOps
Processes
People
Technology
Security and governance
MLOps maturity model
Module 2: Exploring Generative AI Use Cases
Bringing MLOps to experimentation
Setting up the ML experimentation environment
Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio
Workbook: Initial MLOps
Hands-on Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog
Module 3: Repeatable MLOps: Repositories
Managing data for MLOps
Version control of ML models
Code repositories in ML
Module 4: Repeatable MLOps: Orchestration
ML pipelines
Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines
Module 4: Repeatable MLOps: Orchestration (continued)
End-to-end orchestration with AWS Step Functions
End-to-end orchestration with SageMaker Projects
Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects
Using third-party tools for repeatability
Demonstration: Exploring Human-in-the-Loop During Inference
Governance and security
Demonstration: Exploring Security Best Practices for SageMaker
Workbook: Repeatable MLOps
Hands-on Lab: Automating a Workflow with Step Functions
Module 5: Reliable MLOps: Scaling and Testing
Scaling and multi-account strategies
Testing and traffic-shifting
Demonstration: Using SageMaker Inference Recommender
Workbook: Multi-account strategies
Hands-on Lab: Testing Model Variants
Hands-on Lab: Hands-On Lab: Shifting Traffic
Module 6: Reliable MLOps: Monitoring
The importance of monitoring in ML
Operations considerations for model monitoring
Remediating problems identified by monitoring ML solutions
Workbook: Reliable MLOps
Hands-on Lab: Monitoring a Model for Data Drift
Hands-on Lab: Building and Troubleshooting an ML Pipeline
Best Value offer
Official Courseware
Instructor-led live online / Classroom
Flexible Schedule
Course Completion Certification
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