When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
The Machine Learning Pipeline on AWS
This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each...
Read MoreUpcoming Course Dates
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Jan 9, 2024 - Jan 12, 2024 (4 days)
Live Virtual
Language Time English 9:00am – 5:00pm EST Select delivery method/location (1 options) Live Virtual | 9:00am – 5:00pm EST- Enroll
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Jan 30, 2024 - Feb 2, 2024 (4 days)
Live Virtual
Language Time English 12:00pm – 8:00pm EST Select delivery method/location (1 options) Live Virtual | 12:00pm – 8:00pm EST- Enroll
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Feb 13, 2024 - Feb 16, 2024 (4 days)
Live Virtual
Language Time English 9:00am – 5:00pm EST Select delivery method/location (1 options) Live Virtual | 9:00am – 5:00pm EST- Enroll
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Taking ML models from conceptualization to production is typically complex and time-consuming. You have to manage large amounts of data to train the model, choose the best algorithm for training it, manage the compute capacity while training it, and then deploy the model into a production environment. SageMaker reduces this complexity by making it much easier to build and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays. Students will learn about each phase of the pipeline from instructor presentations and demonstrations. They will 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, you will have successfully built, trained, evaluated, tuned, and deployed an ML model that solves selected business problems.
ExitCertified is an AWS Advanced Training Partner, the highest level of training partnership awarded by AWS. ExitCertified provides vendor-approved training and has the largest team of instructors delivering advanced AWS classes in North America, and the deepest bench of instructors delivering the entire authorized AWS catalog. AWS designates its highest status to only those few training partners that have consistently delivered the highest quality experience for learners. In 2021, students rated ExitCertified’s AWS training 4.69 out of 5.
This course explores how to the use of 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.
Skills Gained
- 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
Who Can Benefit
- Developers
- Solutions Architects
- Data Engineers
- Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker
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
Course Details
Day 1
- Module 0: Introduction
- Module 1: Introduction to Machine Learning and the ML Pipeline
- Module 2: Introduction to Amazon SageMaker
- Module 3: Problem Formulation
Day 2
- Module 4: Preprocessing
Day 3
- Module 5: Model Training
- Module 6: Model Evaluation
Day 4
- Module 7: Feature Engineering and Model Tuning
- Module 8: Deployment
