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The Machine Learning Pipeline on AWS

4.6 out of 5 rating

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Duration

4 Days

24 CPD hours

About this course

This course is intended for:
Developers
Solutions Architects
Data Engineers
Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon
SageMaker

Overview

In this course, you will learn 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

Description

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the 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.

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
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
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
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
Additional course details:

Nexus Humans The Machine Learning Pipeline on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the The Machine Learning Pipeline on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Training Insurance Included!

When you organise training, we understand that there is a risk that some people may fall ill, become unavailable. To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.

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