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The Machine Learning Pipeline on AWS

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This Machine Learning Pipeline on AWS course discusses how to use an iterative machine learning process pipeline to handle some real-world business difficulties that many firms confront.

Course description

This Machine Learning Pipeline on AWS 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.

Activities

The course will emphasize a practical learning environment, including group presentations, demonstrations and hands-on labs, to enhance a basic understanding of how AWS machine learning works.

COURSE OBJECTIVES

In this course, you will:

  • Use the ML pipeline to address a particular business issue 
  • Using Amazon SageMaker, you can develop, test, deploy, and fine-tune an ML model. 
  • Describe a few recommended practices for creating scalable, economical, and secure machine learning pipelines in AWS.
  • After completing the course, apply machine learning (ML) to a genuine business issue.

Intended Audience

While this course doesn’t require you to be a tech expert, this course can be beneficial for the following group of people

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

Prerequisites

Attendees of this course are advised to have the following: 

  • Some familiarity with the Python programming language 
  • A fundamental knowledge of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch) 
  • Experience in working with Jupyter notebooks on a basic level

Module Breakdown

Course outline

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

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

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

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

CERTIFICATION : AWS CERTIFIED MACHINE LEARNING - SPECIALTY

This certification allows businesses to hire employees with essential requirements of carrying out some critical cloud activities on AWS cloud. AWS Certified Machine Learning – Specialty status attests to one’s proficiency in creating, honing, optimizing, and deploying machine learning (ML) models on the platform.

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EXAM READINESS: AWS CERTIFIED MACHINE LEARNING - SPECIALTY

Your ability to create, implement, deploy, and maintain Machine Learning (ML) solutions for specific business issues is confirmed by passing the AWS Certified Machine Learning – Specialty exam. Explore the exam’s topic areas, such as data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, in this half-day advanced-level course. The course teaches you how to apply the topics being examined so you may more readily eliminate incorrect solutions. It also discusses how to understand exam questions in each academic area.

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Course Schedule

Course Name Date Register
The Machine Learning Pipeline on AWS 13 Dec - 16 Dec Register

Course Overview

Duration 4D
Modality ViLT/ Classroom
Data Sheet DOWNLOAD
Check Dates CLICK HERE
Price 60,000.00

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