Practical Data Science with Amazon SageMaker
Artificial Intelligence and Machine learning are increasingly dominating the business landscape. This makes it important to learn how to collaborate with data scientists to develop ML-integrated applications. As part of this course, you will learn how data scientists develop solutions on the AWS Cloud with Amazon SageMaker. You will also understand how to develop, train and deploy ML models.
This course is intended for DevOps Engineers and Application developers eager to develop applications that work well with Machine Learning. Entry-level knowledge of Python programming and basic knowledge of statistics will help. The class is delivered with presentations, hands-on labs and demonstrations by an Amazon Authorized Instructor.
This course is intended for DevOps Engineers and Application developers eager to develop applications that work well with Machine Learning. Entry-level knowledge of Python programming and basic knowledge of statistics will help. The class is delivered with presentations, hands-on labs and demonstrations by an Amazon Authorized Instructor.
In this course, you will learn to:
- Discuss the benefits of different types of machine learning for solving business problems
- Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
- Explain how data scientists use AWS tools and ML to solve a common business problem
- Summarize the steps a data scientist takes to prepare data
- Summarize the steps a data scientist takes to train ML models
- Summarize the steps a data scientist takes to evaluate and tune ML models
- Summarize the steps to deploy a model to an endpoint and generate predictions
- Describe the challenges for operationalizing ML models
- Match AWS tools with their ML function
This course is intended for:
- Development Operations (DevOps) engineers
- Application developers
We recommend that attendees of this course have:
- AWS Technical Essentials
- Entry-level knowledge of Python programming
- Entry-level knowledge of statistics
Module 1: Introduction to machine learning
- Types of ML
- Job Roles in ML
- Steps in the ML pipeline
Module 2: Introduction to data prep and SageMaker
- Training and test dataset defined
- Introduction to SageMaker
- Demonstration: SageMaker console
- Demonstration: Launching a Jupyter notebook
Module 3: Problem formulation and dataset preparation
- Business challenge: Customer churn
- Review customer churn dataset
Module 4: Data analysis and visualization
- Demonstration: Loading and visualizing your dataset
- Exercise 1: Relating features to target variables
- Exercise 2: Relationships between attributes
- Demonstration: Cleaning the data
Module 5: Training and evaluating a model
- Types of algorithms
- XGBoost and SageMaker
- Demonstration: Training the data
- Exercise 3: Finishing the estimator definition
- Exercise 4: Setting hyper parameters
- Exercise 5: Deploying the model
- Demonstration: hyper parameter tuning with SageMaker
- Demonstration: Evaluating model performance
Module 6: Automatically tune a model
- Automatic hyper parameter tuning with SageMaker
- Exercises 6-9: Tuning jobs
Module 7: Deployment / production readiness
- Deploying a model to an endpoint
- A/B deployment for testing
- Auto Scaling
- Demonstration: Configure and test auto scaling
- Demonstration: Check hyper parameter tuning job
- Demonstration: AWS Auto Scaling
- Exercise 10-11: Set up AWS Auto Scaling
Module 8: Relative cost of errors
- Cost of various error types
- Demo: Binary classification cutoff
Module 9: Amazon SageMaker architecture and features
- Accessing Amazon SageMaker notebooks in a VPC
- Amazon SageMaker batch transforms
- Amazon SageMaker Ground Truth
- Amazon SageMaker Neo
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 | 1 days | All Day | May 14, 2024 | ₹15,000 | |
| Classroom | 1 days | All Day | May 28, 2024 | ₹15,000 | |
| Classroom | 1 days | All Day | June 11, 2024 | ₹15,000 | |
| Classroom | 1 days | All Day | June 25, 2024 | ₹15,000 |
Don't see a date that works for you?
Fill in the form below to let us know.
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