Machine Learning Ops Engineering on AWS; Stockholm - Virtual Session (AMLO2509SEV)

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.

PRICE

EUR 2,140.00 (excl VAT)

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

RECOMMNEDED COURSE PREREQUISITES:

We recommend that attendees have completed:

• AWS Technical Essentials (classroom or digital)
• DevOps Engineering on AWS, or equivalent experience
• Practical Data Science with Amazon SageMaker, or equivalent experience

THIS COURSE IS DESIGNED TO TEACH YOU HOW 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

AGENDA 

Day 1

• Module 1: Introduction to MLOps 
• Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio
• Module 3: Repeatable MLOps: Repositories 
• Module 4: Repeatable MLOps: Orchestration 

Day 2 

• Module 4: Repeatable MLOps: Orchestration (continued)  
• Module 5: Reliable MLOps: Scaling and Testing 

Day 3

• Module 5: Reliable MLOps: Scaling and Testing (continued) 
• Module 6: Reliable MLOps: Monitoring 

TECHNICAL REQUIREMENTS

You must bring your own laptop device to class in order to run hands-on labs. You are required to bring a device that meets the following requirements:

• A notebook computer with Wi-Fi
• Administrator access to the computer (if Windows, optional)
• Chrome or Firefox (Internet Explorer will not work)

CANCELLATION & PAYMENT POLICY

All prices are net prices. VAT will be added to the price. You have the right to cancel your registration free of charge up until 15 days prior to the event. Nordcloud holds the right to cancel the event 14 days prior to the event, in which case all paid seats will be refunded. A final event confirmation message will be sent to all registrants 14 days prior to the event. No refunds will be paid after the final event confirmation message has been sent. In case the event is cancelled a cancellation message will be sent