The Machine Learning Pipeline on AWS (EN)
- Course Code GK7376
- Duration 4 days
Course Delivery
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Course Delivery
This course is available in the following formats:
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Public Classroom
Traditional Classroom Learning
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Virtual Learning
Learning that is virtual
Request this course in a different delivery format.
Course Overview
TopCourse Schedule
Top-
- Delivery Format: Virtual Learning
- Date: 17-20 November, 2024
- Location: Virtual
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- Delivery Format: Public Classroom
- Date: 24-27 November, 2024
- Location: Riyadh
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- Delivery Format: Public Classroom
- Date: 25-28 November, 2024
- Location: Dubai-Knowledge Village
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- Delivery Format: Virtual Learning
- Date: 25-28 November, 2024
- Location: Virtual
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- Delivery Format: Public Classroom
- Date: 09-12 December, 2024
- Location: Dubai-Knowledge Village
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- Delivery Format: Virtual Learning
- Date: 09-12 December, 2024
- Location: Virtual
Target Audience
TopThis course is intended for:
- Developers
- Solutions Architects
- Data Engineers
- Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker
Course Objectives
TopIn this course, you will learn 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
Course Content
TopDay One
- 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
- Lab 1: Introduction to Amazon SageMaker
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
- Problem Formulation Exercise and Review
- Project work for Problem Formulation
Day Two
Module 4: Preprocessing
- Overview of data collection and integration, and techniques for data preprocessing and visualization
- Lab 2: Data Preprocessing (including project work)
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 Training
- How to evaluate classification models
- How to evaluate regression models
- Practice model training and evaluation
- Train and evaluate project models
- Lab 3: Model Training and Evaluation (including project work)
- Project Share-Out 1
Module 7: Feature Engineering and Model Tuning
- Feature extraction, selection, creation, and transformation
- Hyperparameter tuning
- Demo: SageMaker hyperparameter optimization
Day Three
Recap and Checkpoint #2
Module 6: Model Training
- How to evaluate classification models
- How to evaluate regression models
- Practice model training and evaluation
- Train and evaluate project models
- Lab 3: Model Training and Evaluation (including project work)
- Project Share-Out 1
Module 7: Feature Engineering and Model Tuning
- Feature extraction, selection, creation, and transformation
- Hyperparameter tuning
- Demo: SageMaker hyperparameter optimization
Day Four
Lab 4: Feature Engineering (including project work)
Module 8: Module Deployment
- How to deploy, inference, and monitor your model on Amazon SageMaker
- Deploying ML at the edge
Module 9: Course Wrap-Up
- Project Share-Out 2
- Post-Assessment
- Wrap-up
Course Prerequisites
TopWe recommend that attendees of this course have:
- 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
- #000000
- GK7376
- The Machine Learning Pipeline on AWS (EN)
- Cloud Computing
- GK7376 | The Machine Learning Pipeline on AWS (EN) | Training Course | Amazon Web Services.
- Amazon Web Services