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The Machine Learning Pipeline on AWS

Cost
$2700 USD
Course Code
AWS-ML-PL
Duration
4 Days
Format
Live Virtual Class

Course Schedule

The Machine Learning Pipeline on AWS

$2,700.00

Scheduled, 31-May-2022 - 3-Jun-2022, 09:00 AM - 05:00 PM US Pacific
Scheduled, 27-Jun-2022 - 30-Jun-2022, 10:00 AM - 06:00 PM US Eastern
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The Machine Learning Pipeline on AWS – Scheduled, 31-May-2022 - 3-Jun-2022, 09:00 AM - 05:00 PM US Pacific
$2,700.00
The Machine Learning Pipeline on AWS – Scheduled, 27-Jun-2022 - 30-Jun-2022, 10:00 AM - 06:00 PM US Eastern
$2,700.00
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SKU: AWS-ML-PL Categories: ,

Overview

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of instruction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays.

Audience

  • Developers
  • Solutions architects
  • Data engineers
  • Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning

Suggested Prerequisites

  • Basic knowledge of Python
  • Basic understanding of working in a Jupyter notebook environment
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)

Topics

  • Introduction to Machine Learning and the ML Pipeline, Amazon SageMaker
  • Problem Formulation
  • Preprocessing
  • Model Training
  • Model Evaluation
  • Feature Engineering and Model Tuning
  • Deployment

Overview

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of instruction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays.

Audience

  • Developers
  • Solutions architects
  • Data engineers
  • Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning

Suggested Prerequisites

  • Basic knowledge of Python
  • Basic understanding of working in a Jupyter notebook environment
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)

Topics

  • Introduction to Machine Learning and the ML Pipeline, Amazon SageMaker
  • Problem Formulation
  • Preprocessing
  • Model Training
  • Model Evaluation
  • Feature Engineering and Model Tuning
  • Deployment
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