The Machine Learning Pipeline on AWS; Stockholm - Virtual Session (AMP2406SEV)

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment.

PRICE

EUR 2,790.00 (excl VAT)

ABOUT THE COURSE

This course explores how to use the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.

LANGUAGE

The course will be taught in English.

THIS COURSE IS INTENDED FOR:

• Developers
• Solutions Architects
• Data Engineers
• Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker

COURSE PREREQUISITES:

• Basic knowledge of Python programming language
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
• Basic experience working in a Jupyter notebook environment

THIS COURSE IS DESIGNED TO TEACH YOU HOW TO:

• Select and justify the appropriate ML approach for a given business problem
• Use the ML pipeline to solve a specific business problem
• Train, evaluate, deploy, and tune an ML model using Amazon SageMaker
• Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
• Apply machine learning to a real-life business problem after the course is complete

AGENDA

Day One

• Module 0: Introduction
• Module 1: Introduction to Machine Learning and the ML Pipeline
• Module 2: Introduction to Amazon SageMaker
• Module 3: Problem Formulation

Day Two and Three

• Module 4: Preprocessing
• Module 5: Model Training
• Module 6: Model Evaluation

Day Four

• Module 7: Feature Engineering and Model Tuning
• Module 8: Deployment

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