Amazon SageMaker Studio for Data Scientists
This course empowers data scientists to swiftly prepare, build, train, deploy, and monitor machine learning (ML) models. Seasoned data scientists can benefit from the course by gaining skills to master SageMaker Studio tools, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, enhancing productivity across the ML lifecycle.
We recommend that all students complete the AWS Technical Essentials course before enrolling in this program. Additionally, those without prior experience in data science should complete The Machine Learning Pipeline on AWS and Deep Learning on AWS courses, followed by gaining 1-year on-the-job experience.
We recommend that all students complete the AWS Technical Essentials course before enrolling in this program. Additionally, those without prior experience in data science should complete The Machine Learning Pipeline on AWS and Deep Learning on AWS courses, followed by gaining 1-year on-the-job experience.
In this course, you will learn to:
- Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
- Use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
- And much more
This course is intended for:
- Experienced data scientists who are proficient in ML and deep learning fundamentals.
- Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.
We recommend that all attendees of this course have:
- Experience using ML frameworks
- Python programming experience
- At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
- AWS Technical Essentials digital or classroom training
Module 1: Amazon SageMaker Studio Setup
- JupyterLab Extensions in SageMaker Studio
- Demonstration: SageMaker user interface demo
Module 2: Data Processing
- Using SageMaker Data Wrangler for data processing
- Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
- Using Amazon EMR
- Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
- Using AWS Glue interactive sessions
- Using SageMaker Processing with custom scripts
- Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
- SageMaker Feature Store
- Hands-On Lab: Feature engineering using SageMaker Feature Store
Module 3: Model Development
- SageMaker training jobs
- Built-in algorithms
- Bring your own script
- Bring your own container
- SageMaker Experiments
- Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
Module 4: Model Development (continued)
- SageMaker Debugger
- Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
- Automatic model tuning
- SageMaker Autopilot: Automated ML
- Demonstration: SageMaker Autopilot
- Bias detection
- Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
- SageMaker Jumpstart
Module 5: Deployment and Inference
- SageMaker Model Registry
- SageMaker Pipelines
- Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
- SageMaker model inference options
- Scaling
- Testing strategies, performance, and optimization
- Hands-On Lab: Inferencing with SageMaker Studio
Module 6: Monitoring
- Amazon SageMaker Model Monitor
- Discussion: Case study
- Demonstration: Model Monitoring
Module 7: Managing SageMaker Studio Resources and Updates
- Accrued cost and shutting down
- Updates
Capstone
- Environment setup
- Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
- Challenge 2: Create feature groups in SageMaker Feature Store
- Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
- (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization
- Challenge 5: Evaluate the model for bias using SageMaker Clarify
- Challenge 6: Perform batch predictions using model endpoint
- (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline
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 | 3 days | All Day | May 1, 2024 | ₹45,000 | |
| Classroom | 3 days | All Day | May 15, 2024 | ₹45,000 | |
| Classroom | 3 days | All Day | May 29, 2024 | ₹45,000 | |
| Classroom | 3 days | All Day | June 12, 2024 | ₹45,000 | |
| Classroom | 3 days | All Day | June 26, 2024 | ₹45,000 |
Don't see a date that works for you?
Fill in the form below to let us know.
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