Sold by: DATAGENTIC AI
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. 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.
• Course level: Intermediate
• Duration: 3 days
Overview
Unlock the Power of MLOps: Seamlessly Automate, Secure, and Scale Your Machine Learning Models for Maximum Impact!
Transform your machine learning workflows with our comprehensive MLOps course! Learn to automate every step of the ML lifecycle—from experimentation to deployment—while ensuring robust security, governance, and model integrity. With hands-on experience in Amazon SageMaker and proven best practices for versioning, CI/CD, and performance monitoring, you’ll master the tools and techniques to drive innovation and efficiency in your ML projects. Stay ahead of the curve and discover how to deliver scalable, reliable, and high-performance ML solutions with ease. Enroll today and revolutionize your ML operations!
Activities
This course includes presentations, hands-on labs, demonstrations, knowledge checks, and workbook activities.
Course objectives
In this course, 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
Intended audience
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
Prerequisites We recommend that attendees of this course have:
• AWS Technical Essentials (classroom or digital)
• DevOps Engineering on AWS, or equivalent experience
• Practical Data Science with Amazon SageMaker, or equivalent experience
Course outline
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
Highlights
- Build and Automate End-to-End MLOps Pipelines on AWS Learn how to implement full CI/CD pipelines for machine learning workflows using Amazon SageMaker, automating data preparation, model packaging, deployment, monitoring, and retraining.
- Establish Governance, Security, and Best Practices for ML Lifecycle Understand how to manage model versioning, ensure compliance, and apply security and governance controls across ML assets (data, model, and code).
- Operationalize and Monitor ML Solutions at Scale Gain practical skills to detect model performance degradation, monitor ML applications in production, and trigger automated retraining using MLOps best practices.
Details
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Pricing
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Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.
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