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Python ProgrammingAmazon SageMaker Studio for Data Scientists
Learn to use Amazon SageMaker Studio to boost productivity at every step of the ML lifecycle
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Course : Amazon SageMaker Studio for Data Scientists
Level : Advanced
Type : Live online or Classroom
Length : 3 days
This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.
Learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor.
- Experienced data scientists who are proficient in ML and deep learning fundamentals.
- Basic understanding of Python programming
- Basic knowledge of MLOps
- 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
Instructor-led Virtual online live Training.
- Weekend Classes
- Evening Classes
- Weekdays Classes
Course Outline
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
- Using Amazon EMR
- Using AWS Glue interactive sessions
- Using SageMaker Processing with custom scripts
- Python SDK
- SageMaker Feature Store
- Hands-on Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
- Hands-on Lab: Analyze and prepare data at scale using Amazon EMR
- Hands-on Lab: Data processing using Amazon SageMaker Processing and SageMaker
Module 3: Model Development
- SageMaker training jobs
- Built-in algorithms
- Bring your own script
- Bring your own container
- SageMaker Experiments
- SageMaker Debugger
- Automatic model tuning
- SageMaker Autopilot: Automated ML
- Demonstration: SageMaker Autopilot
- Bias detection
- SageMaker Jumpstart
- Hands-on Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
- Hands-on Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
- Hands-on Lab: Using SageMaker Clarify for Bias and Explainability
Module 4: Deployment and Inference
- SageMaker Model Registry
- SageMaker Pipelines
- SageMaker model inference options
- Scaling
- Testing strategies, performance, and optimization
- Hands-on Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
- Hands-on Lab: Inferencing with SageMaker Studio
Module 5: Monitoring
- Amazon SageMaker Model Monitor
- Discussion: Case study
- Demonstration: Model Monitoring
Module 6: Managing SageMaker Studio Resources and Updates
- Accrued cost and shutting down
- Updates
- Hands-on Lab: Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
- Hands-on Lab: Challenge 2: Create feature groups in SageMaker Feature Store
- Hands-on Lab: Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
- Hands-on Lab: Challenge 4: Use SageMaker Debugger for training performance and model optimization
- Hands-on Lab: Challenge 5: Evaluate the model for bias using SageMaker Clarify
- Hands-on Lab: Challenge 6: Perform batch predictions using model endpoint
- Hands-on Lab: Challenge 7: Automate full model development process using SageMaker Pipeline
Best Value offer
- Official Courseware
- Instructor-led live online / Classroom
- Flexible Schedule
- Course Completion Certification
US$ 1995
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Successfully pass the AIF-C01 in the first attempt
Knowledge Check
Question 1: Which AWS service is best suited for running large-scale distributed training of machine learning models?
- Amazon Polly
- AWS Batch
- Amazon SageMaker Training Jobs
- AWS Glue
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