Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are a part of SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve productivity at every step of the ML lifecycle.
Course Objectives:
In this course, you will learn to:
- Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio
- Key problems that Machine Learning can address and ultimately help solve
- How to train a model using Amazon SageMaker’s built-in algorithms and a Jupyter Notebook instance
- How to publish a model using Amazon SageMaker
- How to integrate the published SageMaker endpoint with an application
- To run a training job, hyperparameter tuning job, and create a model using Amazon SageMaker
- To become familiar with machine learning frameworks, including Apache Spark, TensorFlow, and Apache MXNet
- To gain in-depth knowledge of reinforcement learning with Amazon SageMaker RL
- To understand the use of Jupyter notebooks and their role in collaborative and interactive machine learning development
- To learn how to protect data at rest and in transit using encryption in Amazon SageMaker
- To acquire practical skills for deploying machine learning models using Amazon SageMaker
Course content
Introduction to Amazon SageMaker
- What is Amazon SageMaker?
- Machine Learning with Amazon SageMaker
- Explore and Pre-process Data
- Model Training and Deployment
Get Started with Amazon SageMaker
- Create an Amazon S3 Bucket
- Create an Amazon SageMaker Notebook Instance
- Create a Jupyter Notebook
- Download, Explore, and Transform Data
- Train, Deploy, and Validate Model
- Clean Up
Amazon SageMaker Studio Setup
- Jupyter Lab Extensions in SageMaker Studio
- Demonstration: SageMaker user interface demo
Data Processing - Using SageMaker Data Wrangler for data processing
- Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
- Using Amazon EMR
- Lab: Analyze and prepare data at scale using Amazon EMR
- Using AWS Glue interactive sessions
- Using SageMaker Processing with custom scripts
- Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
- SageMaker Feature Store
- Lab: Feature engineering using SageMaker Feature Store
Model Development
- SageMaker training jobs
- Built-in algorithms
- Bring your own script
- Bring your own container
- SageMaker Experiments
- Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
Model Development (continued)
- SageMaker Debugger
- Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
- Automatic model tuning
- SageMaker Autopilot: Automated ML
- Demonstration: SageMaker Autopilot
- Bias detection
- Lab: Using SageMaker Clarify for Bias and Explain ability SageMaker Jumpstart
Deployment and Inference
- SageMaker Model Registry
- SageMaker Pipelines
- Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
- SageMaker model inference options
- Scaling
- Testing strategies, performance, and optimization
- Lab: Inferencing with SageMaker Studio
Monitoring
- Amazon SageMaker Model Monitor
- Discussion: Case study
- Demonstration: Model Monitoring
Managing SageMaker Studio Resources and Updates
- Accrued cost and shutting down
- Updates
Security in Amazon SageMaker
- Data Protection
- Identity and Access Management
- Logging and Monitoring
- Compliance Validation
To see the full course content Download now
Course Prerequisites
- Experience using ML frameworks
- Python programming experience
- At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
Who can attend
- Experienced data scientists who are proficient in ML and deep learning fundamentals
- Data Scientists
- Machine Learning Engineers
- Data Engineers
- Data Analysts
- Cloud Architects
- Financial Analysts
- Technical Managers
Number of Hours: 25hrs
Certification
Key features
- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Virtual Live Experience
- Preparing for Certification
FAQs
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- One to One Training
- Online Training
- Fastrack & Normal Track
- Resume Modification
- Mock Interviews
- Video Tutorials
- Materials
- Real Time Projects
- Materials
- Preparing for Certification
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