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Overview

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.
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What You'll Learn

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 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
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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

Learning Journey

Want to boost your career in AWS? Click on the roles below to see the learning pathways, specially designed to give you the skills to succeed.

  • Data Scientist
  • Machine Learning Engineer
  • Security Governance Specialist
  • Security Manager

Data Scientist

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Fundamentals

AWS-TE : AWS Technical Essentials

Course Duration

1 Day

Certification

Intermediate

AWS-ML : The Machine Learning Pipeline on AWS

Course Duration

4 Days

Certification

Expert

Machine Learning Engineer

icon-trainocate-certified.png

Fundamentals

AWS-TE : AWS Technical Essentials

Course Duration

1 Day

Certification

Intermediate

AWS-ML : The Machine Learning Pipeline on AWS

Course Duration

4 Days

Certification

Expert

Security Governance Specialist

icon-trainocate-certified.png

Fundamentals

AWS-SE : AWS Security Essentials

Course Duration

1 Day

Certification

Intermediate

AWS-ML : The Machine Learning Pipeline on AWS

Course Duration

4 Days

Certification

Expert

Security Manager

icon-trainocate-certified.png

Fundamentals

AWS-SE : AWS Security Essentials

Course Duration

1 Day

Certification

Intermediate

AWS-ML : The Machine Learning Pipeline on AWS

Course Duration

4 Days

Certification

Expert

Day 1

Module 1: Introduction to Machine Learning and the ML Pipeline

  • 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 

Module 2: Introduction to Amazon SageMaker

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

Module 3: Problem Formulation

  • 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 

Day 2

Checkpoint 1 and Answer Review

Module 4: Preprocessing

  • Overview of data collection and integration, and techniques for data preprocessing and visualization
  • Practice preprocessing
  • Preprocess project data
  • Class discussion about projects 

Day 3

Checkpoint 2 and Answer Review

Module 5: Model Training

  • Choosing the right algorithm
  • Formatting and splitting your data for training
  • Loss functions and gradient descent for improving your model
  • Demo: Create a training job in Amazon SageMaker 

Module 6: Model Evaluation

  • How to evaluate classification models
  • How to evaluate regression models
  • Practice model training and evaluation
  • Train and evaluate project models
  • Initial project presentations 

Day 4

Checkpoint 3 and Answer Review

Module 7: Feature Engineering and Model Tuning

  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning
  • Demo: SageMaker hyperparameter optimization
  • Practice feature engineering and model tuning 
  • Apply feature engineering and model tuning to projects
  • Final project presentations

Module 8: Deployment

  • How to deploy, inference, and monitor your model on Amazon SageMaker
  • Deploying ML at the edge
  • Demo: Creating an Amazon SageMaker endpoint
  • Post-assessment
  • Course wrap-up

Instructors

Scott Duffy

Bestselling Azure & TOGAF® Trainer, Microsoft Azure MVP

4.8 (4.1k)
|
10 Courses
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Scott Duffy

Bestselling Azure & TOGAF® Trainer, Microsoft Azure MVP

4.8 (4.1k)
|
10 Courses
profile-instructor-a.png
AWS Certified Machine Learning - Specialty

Frequently Asked Questions (FAQs)

  • Why get AWS certified?

    AWS certifications validate your expertise in cloud computing and your proficiency in using AWS services.

    These certifications are globally recognized and highly sought after by employers, as they demonstrate your ability to design, deploy, and manage scalable and secure cloud solutions on the AWS platform.

    AWS-certified professionals are in high demand, opening doors to new career opportunities and higher earning potential.

  • What to expect for the examination?

    AWS offers a variety of certification exams at different levels (Foundational, Associate, Professional, and Specialty) covering various domains and services.

    The exams typically consist of multiple-choice and multiple-response questions, and some may include scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is AWS certification valid for?

    AWS certifications are valid for three years from the date of passing the exam.

    To maintain your certification, you will need to recertify by passing the latest version of the exam or completing the AWS Cloud Quest game-based training (if option is applicable).

    Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • Why take this course with Trainocate?

    Here’s what sets us apart:

    - Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 16 countries (and counting!).

    - Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.

    - Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.

    - Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.

    - Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.

    - Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

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Frequently Asked Questions

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Improve yourself and your career by taking this course.

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