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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
US$ 1995
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Successfully pass the AIF-C01 in the first attempt
Knowledge Check
Question 1: Which AWS service is best suited for running large-scale distributed training of machine learning models?
- Amazon Polly
- AWS Batch
- Amazon SageMaker Training Jobs
- AWS Glue
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