Overview

Learn to build, train, evaluate, tune and deploy an ML model using Amazon SageMaker to solve a business problem in a project-based learning environment.

The Machine Learning Pipeline on AWS 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.

Trainocate is an AWS Authorized Training Partner as well as the AWS Global Training Partner of the Year 2023 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.

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Skills Covered

In this course, you will learn 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

Who Should Attend

This AWS 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 Curriculum

Prerequisites

We recommend that attendees of this course have:

  • 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

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Course Modules

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Training Options

Intake: 27 Feb - 1 Mar 2024
Duration: 4 Days
Guaranteed: TBC
Modality: ILT
Price:

RM7,200.00Enroll Now

RM8,700.00Enroll Now

Exam:
Intake: 27 Feb - 1 Mar 2024
Duration: 4 Days
Guaranteed: TBC
Modality: VILT
Price:

RM7,200.00Enroll Now

RM8,700.00Enroll Now

Exam:
Intake: 28-31 May 2024
Duration: 4 Days
Guaranteed: TBC
Modality: ILT
Price:

RM7,200.00Enroll Now

RM8,700.00Enroll Now

Exam:
Intake: 28-31 May 2024
Duration: 4 Days
Guaranteed: TBC
Modality: VILT
Price:

RM7,200.00Enroll Now

RM8,700.00Enroll Now

Exam:
Intake: 6-9 Aug 2024
Duration: 4 Days
Guaranteed: TBC
Modality: ILT
Price:

RM7,200.00Enroll Now

RM8,700.00Enroll Now

Exam:
Intake: 6-9 Aug 2024
Duration: 4 Days
Guaranteed: TBC
Modality: VILT
Price:

RM7,200.00Enroll Now

RM8,700.00Enroll Now

Exam:
Intake: 12-15 Nov 2024
Duration: 4 Days
Guaranteed: TBC
Modality: ILT
Price:

RM7,200.00Enroll Now

RM8,700.00Enroll Now

Exam:
Intake: 12-15 Nov 2024
Duration: 4 Days
Guaranteed: TBC
Modality: VILT
Price:

RM7,200.00Enroll Now

RM8,700.00Enroll Now

Exam:

Exam & Certification

AWS Certified Machine Learning – Specialty. (MLS-C01)

This credential helps organizations identify and develop talent with critical skills for implementing cloud initiatives. Earning AWS Certified Machine Learning – Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

Training & Certification Guide

Frequently Asked Questions