Who should attend
Experienced data scientists who are proficient in ML and deep learning fundamentals.
Prerequisites
We recommend that all attendees of this course have:
Experience using ML frameworks
Python programming experience
At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
AWS Technical Essentials digital or classroom training
In-Person
Online
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 strategies
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
