MLOps Engineering on AWS
This course builds upon and extends the DevOps methodology prevalent in software development to build, train, and deploy machine learning (ML) models. The course 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.
Nästa start: 13 apr. (Live Online)
Om utbildningen
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.
Target audience and prerequisites
This course 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
This course 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
Detaljerad information
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
- 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
- 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
Relaterade kurser
Machine Learning Engineering on AWS
Advanced Generative AI Development on AWS
Developing Generative AI Applications on AWS
Building Streaming Data Analytics Solutions on AWS
Practical Data Science with Amazon SageMaker
Mer än en kurs
Vilken kompetens behöver ni om två år — och har ni den idag? Vi hjälper er planera för framtidens kompetensbehov innan luckorna blir ett problem.
Vet ni vilka kompetensgap som finns i er organisation idag? Vi hjälper er kartlägga nuläget och identifiera vad ni behöver bygga för att möta morgondagens krav.
Ladda kortet med utbildningsdagar i förväg och säkra budgeten innan behovet uppstår. Ni får rabatterade priser, flexibel användning och enklare administration — för hela teamet.
Relaterat innehåll
