Machine Learning Pipeline On AWS (AWS-ML)

Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale.

Orchestrating workflows across each step of the machine learning process (e.g., exploring and preparing data, experimenting with different algorithms and parameters, training and tuning models, and deploying models to production) can take months of coding.

The Machine Learning Pipeline on AWS 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.

Trainocate, an AWS Authorized Training Partner as well as the AWS Global Training Partner of the Year 2022, is trusted by AWS to offer, deliver, and/or incorporate official AWS training, including classroom and digital offerings. Whether your team prefers to learn from live instructors, on-demand courses, or both, ATPs offer a breadth of AWS training options for learners of all levels.

Skills Covered:

• 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