The Machine Learning Pipeline on AWS
Course description
This Machine Learning Pipeline on AWS course explores how to the use of the iterative Machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Learners will learn about each phase of the process 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. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.
Activities
The course will emphasize a practical learning environment, including group presentations, demonstrations and hands-on labs, to enhance a basic understanding of how AWS machine learning works.
COURSE OBJECTIVES
Intended Audience
Prerequisites
Module Breakdown
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
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
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
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 : AWS CERTIFIED MACHINE LEARNING - SPECIALTY
This certification allows businesses to hire employees with essential requirements of carrying out some critical cloud activities on AWS cloud. AWS Certified Machine Learning – Specialty status attests to one’s proficiency in creating, honing, optimizing, and deploying machine learning (ML) models on the platform.
Read MoreEXAM READINESS: AWS CERTIFIED MACHINE LEARNING - SPECIALTY
Your ability to create, implement, deploy, and maintain Machine Learning (ML) solutions for specific business issues is confirmed by passing the AWS Certified Machine Learning – Specialty exam. Explore the exam’s topic areas, such as data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, in this half-day advanced-level course. The course teaches you how to apply the topics being examined so you may more readily eliminate incorrect solutions. It also discusses how to understand exam questions in each academic area.
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Course Schedule
Course Name | Date | Register |
---|---|---|
The Machine Learning Pipeline on AWS | 26 Sep - 29 Sep | Register |
The Machine Learning Pipeline on AWS | 10 Oct - 13 Oct | Register |
The Machine Learning Pipeline on AWS | 24 Oct - 27 Oct | Register |
The Machine Learning Pipeline on AWS | 07 Nov - 10 Nov | Register |
The Machine Learning Pipeline on AWS | 21 Nov - 24 Nov | Register |
The Machine Learning Pipeline on AWS | 12 Dec - 15 Dec | Register |
The Machine Learning Pipeline on AWS | 26 Dec - 29 Dec | Register |