MLOps Engineering on AWS
Описание
MLOps Engineering on AWS*
Learn to bring DevOps-style practices into the building, training, and deployment of ML models
Could your Machine Learning (ML) workflow use some DevOps agility? MLOps Engineering on AWS will help you bring DevOps-style practices into the building, training, and deployment of ML models. ML data platform engineers, DevOps engineers, and developers/operations staff with responsibility for operationalizing ML models will learn to address the challenges associated with handoffs between data engineers, data scientists, software developers, and operations through the use of tools, automation, processes, and teamwork. By the end of the course, go from learning to doing by building an MLOps action plan for your organization.
What you’ll learn
- How to deploy your own models in the AWS Cloud
- How to automate workflows for building, training, testing, and deploying ML models
- The different deployment strategies for implementing ML models in production
- How to monitor for data drift and concept drift that could affect prediction and alignment with business expectations
- And much more
Who should take this course
- ML data platform engineers
- DevOps engineers
- Developers/operations staff with responsibility for operationalizing ML models
What experience you’ll need
Required:
- AWS Technical Essentials course
- DevOps Engineering on AWS course, or equivalent experience
- Practical Data Science with Amazon SageMaker course, or equivalent experience
* — курс проводится онлайн, на английском языке
Программа курса
Day 1
Module 1: Introduction to MLOps
• Processes
• People
• Technology
• Security and governance
• MLOps maturity model
Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio
• Bringing MLOps to experimentation
• Setting up the ML experimentation environment
• Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio
• Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog
• Workbook: Initial MLOps
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
Day 2
Module 4: Repeatable MLOps: Orchestration (continued)
• End-to-end orchestration with AWS Step Functions
• Hands-On Lab: Automating a Workflow with 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
Module 5: Reliable MLOps: Scaling and Testing
• Scaling and multi-account strategies
• Testing and traffic-shifting
• Demonstration: Using SageMaker Inference Recommender
• Hands-On Lab: Testing Model Variants
Day 3
Module 5: Reliable MLOps: Scaling and Testing (continued)
• Hands-On Lab: Shifting Traffic
• Workbook: Multi-account strategiesn.
Module 6: Reliable MLOps: Monitoring
• The importance of monitoring in ML
• Hands-On Lab: Monitoring a Model for Data Drift
• Operations considerations for model monitoring
• Remediating problems identified by monitoring ML solutions
• Workbook: Reliable MLOps
• Hands-On Lab: Building and Troubleshooting an ML Pipeline
Расписание
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Возможно, мы предложим пройти курс в дистанционном режиме или организуем выездной курс, если у Вас группа.