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The Machine Learning Pipeline on AWS

2,700 USD
4 Days
Classroom Training, Online Training
Amazon Web Services

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Take advantage of flexible training options with the AWS Training Pass and get Authorized AWS Training for a full year.

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  • Date: 16-Oct-2023 to 19-Oct-2023
    Time: 12PM - 7PM US Eastern
    Location: Virtual
    Language: Spanish
    Delivered by: LearnQuest
    Price: 2,700 USD
  • Date: 30-Oct-2023 to 2-Nov-2023
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,700 USD
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Course Description


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.
  • Duration: 4 days


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


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


    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


  • Day 1
    • Module 0: Introduction
      • Pre-assessment
    • 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
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