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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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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:
AW230 Developing on AWS™ or equivalent hands-on experience
AW233 Practical Data Science with Amazon SageMaker or equivalent hands-on experience
A prior participation in the following AWS™ trainings is recommended:
The Elements of Data Science or equivalent hands-on experience
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.
Guaranteed implementation:
from 2 Attendees
Booking information:
Duration:
3 Days
Price:
1.995,00 € plus VAT.
For in-person attendance, lunch and beverages are included in the price.
No appointment available
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