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AW234: MLOps Engineering on AWS™

Training: AWS™ - Cloud - Artificial Intelligence - Certifications

Participants receive a practical introduction to MLOps on AWS™. The course covers methods for building ML deployments based on the MLOps maturity model with a focus on data, models, and code. It addresses automation, processes, and collaboration to integrate data engineers, data scientists, and developers, as well as monitoring model predictions using defined KPIs.

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Possible course languages: German

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Agenda:

Introduction to MLOps
- Processes
- People
- Technology
- Security and governance
- MLOps maturity model

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

Repeatable MLOps: Repositories
- Managing data for MLOps
- Version control of ML models
- Code repositories in ML

Repeatable MLOps: Orchestration
- ML pipelines
- Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines
- 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

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
- Hands-On Lab: Shifting Traffic
- Workbook: Multi-account strategies

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

Objectives:

In this course AW234 MLOps Engineering on AWS™, you will learn to:

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 environments for MLOps with Amazon SageMaker
Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code)
Describe three options for creating a full CI/CD pipeline in an ML context
Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code)
Demonstrate how to monitor ML based solutions
Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion; detects performance degradation; and re-trains the model on top of newly acquired data

Target audience:

This course AW234 MLOps Engineering on AWS™ is intended for:

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

Prerequisites:

To participate in the course AW234 MLOps Engineering on AWS™ at qSkills™, you should have completed the following AWS™ trainings:

AW110 AWS™ Technical Essentials
AW230 Developing on AWS™
AW233 Practical Data Science with Amazon SageMaker

A prior participation in the following AWS™ trainings is recommended:

The Elements of Data Science
Machine Learning Terminology and Process

Description:

This course AW234 MLOps Engineering on AWS™ builds upon and extends the DevOps methodology prevalent in software development to build, train, and deploy machine learning (ML) models. The course AW234 MLOps Engineering on AWS™ is based on the four-level MLOPs maturity framework. The course focuses on the first three levels, including the initial, repeatable, and reliable levels. The course stresses the importance of data, model, and code to successful ML deployments. It demonstrates the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course also discusses the use of tools and processes to monitor and take action when the model prediction in production drifts from agreed-upon key performance indicators.

This course W234 MLOps Engineering on AWS™ includes presentations, hands-on labs, demonstrations, knowledge checks, and workbook activities.