The Machine Learning Pipeline on AWS
The course explores the usage of the iterative Machine Learning (ML) pipeline to solve real-world business problems in a project-based environment. You will learn about each phase of the pipeline from an experienced AWS instructor via live presentations and demonstrations. You will then go on to complete a project while solving one of the three business problems such as fraud detection, recommendation engines or flight delays. By the end of this course you will have built, trained, evaluated and deployed a ML model using Amazon SageMaker to solve a selected business problem. It also prepares you for the AWS Certified Machine Learning – Speciality certification.
This course is recommended for developers, solution architects, data engineers and anyone who wishes to learn more about the ML pipeline using Amazon SageMaker. We recommend you to have basic knowledge of Python programming language, basic understanding of AWS cloud services and basic experience of working in a Jupyter notebook environment.
This course is recommended for developers, solution architects, data engineers and anyone who wishes to learn more about the ML pipeline using Amazon SageMaker. We recommend you to have basic knowledge of Python programming language, basic understanding of AWS cloud services and basic experience of working in a Jupyter notebook environment.
In this course, you will:
- 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
This 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
We 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
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
Why choose Cloud Wizard
- Advanced Tier Training Partner
- Amazon Authorised Instructors
- Official AWS Content
- Hands-on Labs
Class Deliverables
- E-Content kit by AWS
- Hands-on labs
- Class completion certificates
- Exam Prep sessions
Dates Available
Choose a date that works for you and click on Book Now to proceed with your registration.
| Method | Duration | Start Time | Start date | Price | Action |
|---|---|---|---|---|---|
| Classroom | 4 days | All Day | May 7, 2024 | ₹60,000 | |
| Classroom | 4 days | All Day | May 21, 2024 | ₹60,000 | |
| Classroom | 4 days | All Day | June 4, 2024 | ₹60,000 | |
| Classroom | 4 days | All Day | June 18, 2024 | ₹60,000 |
Don't see a date that works for you?
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