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The Machine Learning Pipeline on AWS Training


The Machine Learning Pipeline on AWS Training demonstrates the methodologies to utilize machine learning pipeline to rectify real business issues in a project-based learning environment.

  • Category : AWS

Course Price : $2800 Per Participant

Course Description

The Machine Learning Pipeline on AWS Training demonstrates the methodologies to utilize the machine learning pipeline to rectify real business issues in a project-based learning environment.

This technical course helps professionals gain comprehensive knowledge on each phase of the pipeline from the demonstrations and preparations and utilizing that information to finish a project.

It trains individuals to build, evaluate, train, and launch an ML model with the help of Amazon SageMaker that rectifies their major business issues.

This course teaches you to apply machine learning to a real-time issue and use the ML pipeline to overcome a specific business problem.

You will demonstrate some of the leading practices for creating scalable, cost-effective, and protected ML pipelines in AWS.

It enables you to choose and justify the suitable ML approach for a stated business problem.

This course is ideal for developers, solution engineers, or anyone who wants to gain more information related to ML pipelines using Amazon SageMaker. 

Microsoft Course Microsoft Course


experience experience

Years of Experience

learners learners

Global Learners

What you will learn

  • green-tick Utilizing ML pipeline to solve the specified business problems.
  • green-tick Utilizing the ML pipeline to resolve an exact business troubles.
  • green-tick Deploying, Training, evaluating, and tuning a ML model in Amazon SageMaker.
  • green-tick Applying ML to real-life business difficulties after the competition of the course.
  • green-tick Justifying and selecting appropriate ML approaches for particular business difficulties.
  • green-tick Describing few most excellent training so as to secure ML pipelines, cost-optimized, designing scalable in AWS.

Who should attend this course?

The Machine Learning Pipeline On AWS Training is highly advantageous for Data Engineers, Solutions Architects, and Developers who have little experience with Machine learning fundamentals and ML pipelines.

Therefore, professionals who are utilizing Amazon SageMaker and have brief knowledge about SageMaker can enroll in this training.


  • Dec 18, 2023
  • 9:00 am - 5:00 pm EST
  • online

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  • 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
  • Introduction to Amazon SageMaker
  • Demo: Amazon SageMaker and Jupyter notebooks
  • Lab: Introduction to Amazon SageMaker
  • 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
  • Problem Formulation Exercise and Review
  • Project work for Problem Formulation
  • Overview of data collection and integration, and techniques for data preprocessing and visualization
  • Lab: Data Preprocessing (including project work)
  • 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
  • How to evaluate classification models
  • How to evaluate regression models
  • Practice model training and evaluation
  • Train and evaluate project models
  • Lab: Model Training and Evaluation (including project work)
  • Project Share-Out 1
  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning
  • Demo: SageMaker hyperparameter optimization
  • Lab: Feature Engineering (including project work)
  • How to deploy, inference, and monitor your model on Amazon SageMaker
  • Deploying ML at the edge
  • Project Share-Out 2
  • Post-Assessment
  • Wrap-up
  • Course Details

    • enroll enroll-green
      Enrolled: 1642
    • duration duration green
      Duration: 4 Days

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