Practical Data Science with Amazon SageMaker
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?
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