The Machine Learning Pipeline on AWS

Course subjects

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 visualisation

• Practice preprocessing

• Preprocess project data and discuss project progress

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, then present findings

Module 7: 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 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

Please note: This is an emerging technology course. Course outline is subject to change as needed.