Centriq Training Print Logo

Sunday

September 25 06:24 AM

AWS-MLP

The Machine Learning Pipeline on AWS Training Class:

  • Ways to Train:
  • Live Classroom
    Class is delivered at a Centriq location with a live instructor actually in the classroom.
  • Live Virtual Class
    Class is delivered live online via Centriq's Virtual Remote technology. Student may attend class from home or office or other location with internet access.
  • HD Class
    Class is delivered via award winning HD-ILT at Centriq's facility. Students view the live instructor utilizing a 60'' HD monitor.
  • Ways to Buy:
  • Retail
    Class can be purchased directly via check, credit card, or PO.
  • CV Centriq Vouchers
    Class is available for students using Centriq Vouchers.
  • CP Centriq Choice Pass Eligible
    Class is available to students utilizing Centriq Choice Pass program.
Start Date End Date Duration Days Start Time End Time Time Zone Location Ways to Train Ways to Buy Price
Request a Date
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.

Audience

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

Prerequisites

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

Course Completion

In this course, you will:

  • 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

View the The Machine Learning Pipeline on AWS Training Course Outline

Module 1: Introduction to Machine Learning and the ML Pipeline

Module 2: Introduction to Amazon SageMaker

Module 3: Problem Formulation

Module 4: Preprocessing

Module 5: Model Training

Module 6: Model Evaluation

Module 7: Feature Engineering and Model Tuning

Module 8: Deployment

Enroll Now!