Course
The Machine Learning Pipeline on AWS – Intensive Training («AWSA07»)
This course prepares you to get certified on 'AWS Certified Machine Learning (Specialty Level)'. You will explore how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment.
Duration
4 days
Price
3'700.–
Course documents
Digital original AWS courseware
Relevant Job Roles:
Machine Learning & AI
Course facts
- Selecting and justifying the appropriate ML approach for a given business problem
- Using the ML pipeline to solve a specific business problem
- Training, evaluating, deploying, and tuning an ML model using Amazon SageMaker
- Describing some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
- Applying machine learning to a real-life business problem after the course is complete
Participants learn about each phase of the pipeline through presentations and demonstrations by the trainers and apply this knowledge to implement a project to solve 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.
Course outline:
Day One
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.
Course outline:
Day One
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 Two
Checkpoint 1 and Answer Review
Module 4: Preprocessing
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 Three
Checkpoint 2 and Answer Review
Module 5: Model Training
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 Four
Checkpoint 3 and Answer Review
Module 7: Feature Engineering and Model Tuning
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
This course is intended for the following job roles:
- Machine Learning & AI
We recommend that attendees of this course have the following prerequisites:
- 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
and have attended the following course (or equivalent knowlege):