Course Overview

This Machine Learning Pipeline on AWS course from CloudThat teaches candidates how to use the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Candidates taking up this Machine Learning Pipeline on AWS training also learn to solve one of three business problems, including recommendation engines, fraud detection, or flight delays. By the end of the Machine Learning Pipeline AWS training and certification course, candidates are successfully trained to build, evaluate, tune, and deploy an ML model using Amazon SageMaker. Candidates with basic knowledge of Statistics will find this course helpful. This course comprises presentations, group exercises, demonstrations, and hands-on labs.

After completing this course, students will be able to:

  • 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

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Key Features

  • Our training modules have 50% - 60% hands-on lab sessions to encourage Thinking-Based Learning (TBL).
  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).
  • AWS certified instructor-led training and mentoring sessions to develop Competency-Based Learning (CBL).
  • Well-structured use-cases to simulate challenges encountered in a Real-World environment.
  • Being an authorized AWS Training Partner gives us an edge over competition.

Who Should Attend

  • 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

We recommend that attendees of this Machine Learning Pipeline on AWS course must 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

Course Outline Download Course Outline

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

Certification

    • By earning Machine Learning pipeline on AWS certification, you will show your future or current employer that you have knowledge of AWS Cloud concepts.
    • Machine Learning pipeline on AWS certification can be used to learn usage of iterative machine learning (ML) process pipelines
    • On successful completion of Machine Learning pipeline on AWS certification training aspirants receive a Course Completion Certificate from us
    • By successfully clearing the Machine Learning pipeline on AWS certification exams, aspirants earn AWS Certification

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Course ID: 13504

Course Price at

₹34900 + 18% GST

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