Getting started with Amazon SageMaker Studio learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
Build production-grade machine learning models with Amazon SageMaker Studio, the first integrated development environment in the cloud, using real-life machine learning examples and code Key Features Understand the ML lifecycle in the cloud and its development on Amazon SageMaker Studio Learn to app...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited
[2022]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009657507406719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- Part 1 - Introduction to Machine Learning on Amazon SageMaker Studio
- Chapter 1: Machine Learning and Its Life Cycle in the Cloud
- Technical requirements
- Understanding ML and its life cycle
- An ML life cycle
- Building ML in the cloud
- Exploring AWS essentials for ML
- Compute
- Storage
- Database and analytics
- Security
- Setting up an AWS environment
- Summary
- Chapter 2: Introducing Amazon SageMaker Studio
- Technical requirements
- Introducing SageMaker Studio and its components
- Prepare
- Build
- Training and tuning
- Deploy
- MLOps
- Setting up SageMaker Studio
- Setting up a domain
- Walking through the SageMaker Studio UI
- The main work area
- The sidebar
- Hello world!" in SageMaker Studio
- Demystifying SageMaker Studio notebooks, instances, and kernels
- Using the SageMaker Python SDK
- Summary
- Part 2 - End-to-End Machine Learning Life Cycle with SageMaker Studio
- Chapter 3: Data Preparation with SageMaker Data Wrangler
- Technical requirements
- Getting started with SageMaker Data Wrangler for customer churn prediction
- Preparing the use case
- Launching SageMaker Data Wrangler
- Importing data from sources
- Importing from S3
- Importing from Athena
- Editing the data type
- Joining tables
- Exploring data with visualization
- Understanding the frequency distribution with a histogram
- Scatter plots
- Previewing ML model performance with Quick Model
- Revealing target leakage
- Creating custom visualizations
- Applying transformation
- Exploring performance while wrangling
- Exporting data for ML training
- Summary
- Chapter 4: Building a Feature Repository with SageMaker Feature Store
- Technical requirements
- Understanding the concept of a feature store.
- Understanding an online store
- Understanding an offline store
- Getting started with SageMaker Feature Store
- Creating a feature group
- Ingesting data to SageMaker Feature Store
- Ingesting from SageMaker Data Wrangler
- Accessing features from SageMaker Feature Store
- Accessing a feature group in the Studio UI
- Accessing an offline store - building a dataset for analysis and training
- Accessing online store - low-latency feature retrieval
- Summary
- Chapter 5: Building and Training ML Models with SageMaker Studio IDE
- Technical requirements
- Training models with SageMaker's built-in algorithms
- Training an NLP model easily
- Managing training jobs with SageMaker Experiments
- Training with code written in popular frameworks
- TensorFlow
- PyTorch
- Hugging Face
- MXNet
- Scikit-learn
- Developing and collaborating using SageMaker Notebook
- Summary
- Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify
- Technical requirements
- Understanding bias, fairness in ML, and ML explainability
- Detecting bias in ML
- Detecting pretraining bias
- Mitigating bias and training a model
- Detecting post-training bias
- Explaining ML models using SHAP values
- Summary
- Chapter 7: Hosting ML Models in the Cloud: Best Practices
- Technical requirements
- Deploying models in the cloud after training
- Inferencing in batches with batch transform
- Hosting real-time endpoints
- Optimizing your model deployment
- Hosting multi-model endpoints to save costs
- Optimizing instance type and autoscaling with load testing
- Summary
- Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot
- Technical requirements
- Launching a SageMaker JumpStart solution
- Solution catalog for industries
- Deploying the Product Defect Detection solution
- SageMaker JumpStart model zoo.
- Model collection
- Deploying a model
- Fine-tuning a model
- Creating a high-quality model with SageMaker Autopilot
- Wine quality prediction
- Setting up an Autopilot job
- Understanding an Autopilot job
- Evaluating Autopilot models
- Summary
- Further reading
- Part 3 - The Production and Operation of Machine Learning with SageMaker Studio
- Chapter 9: Training ML Models at Scale in SageMaker Studio
- Technical requirements
- Performing distributed training in SageMaker Studio
- Understanding the concept of distributed training
- The data parallel library with TensorFlow
- Model parallelism with PyTorch
- Monitoring model training and compute resources with SageMaker Debugger
- Managing long-running jobs with checkpointing and spot training
- Summary
- Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor
- Technical requirements
- Understanding drift in ML
- Monitoring data and performance drift in SageMaker Studio
- Training and hosting a model
- Creating inference traffic and ground truth
- Creating a data quality monitor
- Creating a model quality monitor
- Reviewing model monitoring results in SageMaker Studio
- Summary
- Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry
- Technical requirements
- Understanding ML operations and CI/CD
- Creating a SageMaker project
- Orchestrating an ML pipeline with SageMaker Pipelines
- Running CI/CD in SageMaker Studio
- Summary
- Index
- Other Books You May Enjoy.
