The Machine Learning Pipeline on AWS

Duration: 3 Days (24 Hours)

The Machine Learning Pipeline on AWS Course Overview:

The Machine Learning Pipeline on AWS course provides a project-based learning environment to explore the iterative machine learning (ML) process pipeline for solving real business problems. Participants will learn about each phase of the pipeline through instructor presentations and demonstrations. They will then apply this knowledge to complete a project focusing on fraud detection, recommendation engines, or flight delays.

Throughout the course, participants will build, train, evaluate, tune, and deploy an ML model using Amazon SageMaker to solve their chosen business problem. The course is designed to be accessible for learners with limited or no machine learning experience. A basic understanding of statistics will be helpful for the course.

By the end of the course, participants will have gained practical experience in the end-to-end ML pipeline and successfully developed an ML model using Amazon SageMaker to address their selected business problem

Course level: Intermediate

Intended audience:

  • Developers
  • Solutions Architects
  • Data Engineers
  • Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker

Module 0: Introduction
  • Pre-assessment
  • 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
  • Hands-on: Amazon SageMaker and Jupyter notebooks
  • 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
  • Overview of data collection and integration, and techniques for data preprocessing and visualization
  • Practice preprocessing
  • Preprocess project data
  • Class discussion about projects
  • 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
  • Initial project presentations
  • 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
  • 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

We recommend that attendees of this Machine Learning Pipeline on AWS 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
Q: What is The Machine Learning Pipeline on AWS training?

A: The Machine Learning Pipeline on AWS training is a comprehensive program designed to provide individuals with the knowledge and skills needed to build and deploy end-to-end machine learning pipelines using Amazon Web Services (AWS). It covers various aspects of the machine learning workflow, including data preparation, model training, evaluation, deployment, and monitoring using AWS services such as Amazon SageMaker, AWS Glue, and Amazon CloudWatch.

A: This training is suitable for data scientists, machine learning engineers, software developers, and anyone interested in building machine learning pipelines on the AWS platform. It is beneficial for those who want to enhance their understanding of the end-to-end machine learning process and gain hands-on experience with AWS services for developing scalable and reliable machine learning pipelines.

A: The training covers a range of topics, including data preprocessing and feature engineering, model training and evaluation, model deployment and serving, pipeline automation with AWS services, model monitoring and retraining, and best practices for building efficient and robust machine learning pipelines on AWS.

A: While there are no strict prerequisites, having a basic understanding of machine learning concepts, Python programming, and AWS services will be helpful in understanding the content effectively. Familiarity with machine learning frameworks like TensorFlow or PyTorch is also advantageous.

A: To prepare for the training, it is recommended to have a good understanding of machine learning concepts, Python programming, and machine learning frameworks. Familiarizing yourself with AWS services like Amazon SageMaker, AWS Glue, and Amazon CloudWatch will also be beneficial. Exploring relevant online resources, tutorials, and documentation can further enhance your preparation.

A: Yes, we offers online training options for The Machine Learning Pipeline on AWS to provide flexibility for learners.

A: While The Machine Learning Pipeline on AWS training provides valuable knowledge and skills in building end-to-end machine learning pipelines, it does not directly prepare you for specific AWS certifications. However, it lays a solid foundation for pursuing advanced certifications related to machine learning or AWS architecture.

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This course comes with following benefits:

  • Practice Labs.
  • Get Trained by Certified Trainers.
  • Access to the recordings of your class sessions for 90 days.
  • Digital courseware
  • Experience 24*7 learner support.

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