Falsh Sale
kh logo
All Courses
  1. Home
  2. Cloud Computing
  3. The Machine Learning Pipeline on AWS Training

The Machine Learning Pipeline on AWS Training

Machine Learning Pipeline on AWS

Solve business problems with AI with our Machine Learning Pipeline on AWS course.

Enrollment39,282+ Enrolled
4.8/5
social icon image
4.7/5
4.9/5
The Machine Learning Pipeline on AWS Training
  • 450K+
    Professionals trained
  • 250+
    Workshops every month
  • 100+
    Countries and counting

Machine Learning Pipeline on AWS Training Highlights

Reduce Risks with Machine Learning Pipelines

4-Day Live Instructor-Led Training

Training by Amazon Certified Trainers

Case-Study-Based Discussion and Labs

Understand Best Practices for Cloud Adoption


Get Exam Prep Support for the AWS Certification

Latest Curriculum Developed by AWS Experts

The Machine Learning Pipeline on AWS course helps you understand how to use the Machine Learning Pipeline to solve real business problems in a project-based environment. Understand the three major business problems – Fraud Detection, Recommendation Engines, or Flight Delays and learn about the various phases of the pipeline to minimize problems and risks.

This four-day training includes Instructor-led training, hands-on labs, demonstrations, and group exercises. By the end of the course, you will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves your selected business problem. This course will also help you prepare for the AWS Certified Machine Learning – Specialty exam. This course is offered by AWS, and KnowledgeHut is an AWS Training Partner.

Why KnowledgeHut for Machine Learning Pipeline on AWS

Get The KnowledgeHut Advantage

Learn from Industry Experts

Interact with certified instructors who are also industry experts. Listen, Learn, Explore, and apply!

Advanced Curriculum

Gain up-to-date skills and master concepts effortlessly with the most current curriculum.

Hands-On Training

Learn with the help of theory-backed case studies, hands-on exercises, and guided coding practice.

Exam Support

Get a comprehensive support to help you clear the AWS Certified Machine Learning – Specialty exam.

Advance from the Basics

Build a strong foundation learning concepts from scratch through step-by-step guidance.

Job-Ready Skills

Maximize your readiness for a rewarding career as you acquire skills that make you in-demand.

Explore our Schedules

Schedules
No Results
IMage
Ready to master the Machine Learning Pipeline on AWS?

PREREQUISITES FOR Machine Learning Pipeline on AWS Training

Prerequisites and Eligibility

Prerequisites for the ML Pipeline on AWS

  • Basic knowledge of Python programming language
  • Basic understanding of AWS Cloud infrastructure
  • Basic experience working in a Jupyter Notebook environment

For more details, please refer to the FAQs

Prerequisites

Machine Learning Pipeline on AWS CURRICULUM

Curriculum

1. Introduction to Machine Learning and the ML Pipeline

Learning Objective:

Upon completing this module, students will gain a foundational understanding of machine learning, including its core concepts, real-world applications, and the essential stages of a machine learning project. Additionally, they will be introduced to the course's structure and expectations.

Topics:

  • Overview of machine learning, including use cases, types of machine learning, and key concepts
  • Overview of the ML pipeline
  • Introduction to course projects and approach

2. Introduction to Amazon SageMaker

Learning Objective:

Students will gain hands-on experience with Amazon SageMaker and Jupyter Notebooks, including launching instances, writing code, and exploring the platform's functionalities.

Topics:

  • Introduction to Amazon SageMaker
  • Demo: Amazon SageMaker and Jupyter notebooks
  • Hands-on: Amazon SageMaker and Jupyter notebooks

3. Problem Formulation

Learning Objective:

Understand and apply the process of converting business problems into machine learning problems, including deciding when ML is the appropriate solution, with hands-on experience using Amazon SageMaker Ground Truth.

Topics:

  • Overview of problem formulation and deciding if ML is the right solution
  • Converting a business problem into an ML problem
  • Demo: Amazon SageMaker Ground Truth
  • Hands-on: Amazon SageMaker Ground Truth
  • Practice problem formulation
  • Formulate problems for projects

What You Will Learn in Machine Learning Pipeline on AWS training

Learning Objectives
Selecting the Appropriate ML Approach

Learn to select and justify the appropriate ML approach for a given business problem

Solving Business Problems

Gain insights into real world applications of ML Pipeline solutions for specific business problems

Implementing AWS SageMaker

Train, evaluate, deploy, and tune an ML model using Amazon SageMaker

Understanding the Types of Business Problems

Learn to identify fraud detections, recommendation engines, or flight delays

Designing ML Pipelines

Describe some of the best practices for designing scalable, cost-optimized ML pipelines

Applying ML Best Practices

Understand and apply the best practices for scalable and secure Machine Learning Pipelines in AWS

Who Can Attend the Machine Learning Pipeline on AWS Training

Who This Course is For
  • Developers
  • Solutions Architects
  • Data Engineers
  • Anyone seeking to learn about the ML pipeline using Amazon SageMaker

Machine Learning Pipeline on AWS FAQs

Frequently Asked Questions
About the Training

1. What is this course all about?

The Machine Learning Pipeline on AWS Course is an intermediate-level course that explores how to use the machine learning (ML) pipeline to solve a significant business problem in a project-based learning environment.

2. Why is the ML on AWS course relevant?

The Machine Learning Pipeline on AWS course helps you understand how to use Machine Learning Pipeline to solve genuine business problems in a project-based environment. With this course, you will understand the three major business problems – Fraud Detection, Recommendation Engines or Flight Delays and learn about the various phases of the pipeline to minimize problems and risks.

By the end of the course, you will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.

3. What are the practical skills I can acquire after completing this course?

After completing the Machine Learning Pipeline on AWS certification training, you will be able 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
Contact Learning Advisor
Need more information?
Have more questions or need personalized guidance?

RECOMMENDED COURSES FOR CLOUD PROFESSIONALS

Learners Also Enrolled For