A tanfolyamról
The 32 hours Machine Learning Pipeline on AWS course helps you understand how to use the Machine Learning Pipeline to solve real business problems in a project-based environment. Understand the three major business problems – Fraud Detection, Recommendation Engines or Flight Delays and learn about the various phases of the pipeline to minimize problems and risks.
This training includes Instructor-led training, hands-on labs, demonstrations, and group exercises. By the end of the course, you will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
Who Should Attend the Course
- Developers
- Solutions architects
- Data Engineers
- Anyone looking to learn about the ML pipeline using Amazon SageMaker
What You Will Learn
-
Selecting the Appropriate ML Approach
Learn to select and justify the appropriate ML approach for a given business problem -
Solving Business Problems
Gain insights into real world applications of ML Pipeline solutions for specific business problems -
Implementing AWS SageMaker
Train, evaluate, deploy, and tune an ML model using Amazon SageMaker -
Understanding the Types of Business Problems
Learn to identify fraud detections, recommendation engines, or flight delays -
Designing ML Pipelines
Describe some of the best practices for designing scalable, cost-optimized ML pipelines -
Applying ML Best Practices
Understand and apply the best practices for scalable and secure Machine Learning Pipelines in AWS
After completing the Machine Learning Pipeline on AWS certification training, you will be able 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
We provide this course in English.
Tematika
Curriculum
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
2. Introduction to Amazon SageMaker
- Introduction to Amazon SageMaker
- Demo: Amazon SageMaker and Jupyter notebooks
- Hands-on: Amazon SageMaker and Jupyter notebooks
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
4. Pre-processing
- Overview of data collection and integration, and techniques for data pre-processing and visualization
- Practice pre-processing
- Pre-process project data
- Class discussion about projects
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
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
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
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
Kinek ajánljuk
Előfeltételek
Prerequisites
- Basic knowledge of Python programming language
- Basic understanding of AWS Cloud infrastructure
- Basic experience working in a Jupyter notebook environment