AWS
Enjoy the best in The Machine Learning Pipeline on AWS Training
Learn how to use the machine learning (ML) pipeline with Amazon SageMaker through hands-on exercises and four days of instruction. Frame your business problems as ML problems using Amazon SageMaker. Learn how to train, evaluate, tune, and deploy ML models. You’ll also choose a ML project to work on. You will apply the knowledge and skills you learn to your chosen project in each phase of the pipeline.
The Machine Learning Pipeline on AWS
This course is designed to teach you how to:
- Select an appropriate ML approach for a business problem
- How to use the ML pipeline to solve a specific business problem
- How to train, evaluate, deploy, and tune an ML model in Amazon SageMaker
- The best practices for designing scalable, cost-optimised, and secure ML pipelines in AWS
- How to apply ML to a real-life problem in your business
Course Objectives
Module 1
- Introduction
- Pre-assessment.
Module 2
- 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 3
- Introduction to Amazon SageMaker
- Introduction to Amazon SageMaker
- Demo and hands- on lab: Amazon SageMaker and Jupyter notebooks
Module 4
- Problem Formulation
- Overview of problem formulation and deciding if ML is the right solution
- Converting a business problem into an ML problem
- Demo and hands- on lab: Amazon SageMaker Ground Truth
- Practice problem formulation
- Formulate problems for projects
Module 5
- Preprocessing
- Overview of data collection and integration, and techniques for data preprocessing and visualisation
- Practice preprocessing
- Preprocess project data and discuss project progress
Module 6
- 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 7
- Model Evaluation
- How to evaluate classification models
- How to evaluate regression models
- Practice model training and evaluation
- Train and evaluate project models, then present findings
Module 8
- Feature Engineering and Model Tuning
- Feature extraction, selection, creation, and transformation
- Hyperparameter tuning
- Demo: SageMaker hyperparameter optimisation
- Practice feature engineering and model tuning
- Apply feature engineering and model tuning to projects
- Final project presentations
Module 9
- 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
Audience
- This course is intended for: 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.
- We recommend that attendees of this course have: Basic knowledge of Python, Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch),
Prerequisites
We recommend that attendees of this course have:
- Basic knowledge of Python
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Basic understanding of working in a Jupyter notebook environment
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