AW234: MLOps Engineering on AWS™

Training: AWS™ - Cloud - Artificial Intelligence - Certifications

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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:
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.
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Guaranteed implementation:

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Booking information

Price:

1.995,00 € plus VAT.

(including lunch & drinks)

Authorized training partner

NetApp Partner Authorized Learning
Commvault Training Partner
CQI | IRCA Approved Training Partner
Veeam Authorized Education Center
Acronis Authorized Training Center
AWS Partner Select Tier Training
ISACA Accredited Partner
iSAQB
CompTIA Authorized Partner
EC-Council Accredited Training Center

Memberships

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TeleTrust Pioneers in IT security
Bundesverband der IT-Sachverständigen und Gutachter e.V.
Bundesverband mittelständische Wirtschaft (BVMW)
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NIK - Netzwerk der Digitalwirtschaft
BVSW
Bayern Innovativ
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CAST
IHK Nürnberg für Mittelfranken
eato e.V.
Sicherheitsnetzwerk München e.V.