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 codeKey FeaturesUnderstand the ML lifecycle in the cloud and its development on Amazon SageMaker StudioLearn to apply SageMaker features in SageMaker Studio for ML use casesScale and operationalize the ML lifecycle effectively using SageMaker StudioBook DescriptionAmazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment. In this book, you'll start by exploring the features available in Amazon SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals. As you progress, you will learn how these features work together to address common challenges when building ML models in production. After that, you'll understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio. By the end of this book, you'll have learned ML best practices regarding Amazon SageMaker Studio, as well as being able to improve productivity in the ML development life cycle and build and deploy models easily for your ML use cases.What you will learnExplore the ML development life cycle in the cloudUnderstand SageMaker Studio features and the user interfaceBuild a dataset with clicks and host a feature store for MLTrain ML models with ease and scaleCreate ML models and solutions with little codeHost ML models in the cloud with optimal cloud resourcesEnsure optimal model performance with model monitoringApply governance and operational excellence to ML projectsWho this book is forThis book is for data scientists and machine learning engineers who are looking to become well-versed with Amazon SageMaker Studio and gain hands-on machine learning experience to handle every step in the ML lifecycle, including building data as well as training and hosting models. Although basic knowledge of machine learning and data science is necessary, no previous knowledge of SageMaker Studio and cloud experience is required. 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 Developers working with machine learning will be able to put their knowledge to work with this practical guide to Amazon SageMaker Studio. The book takes a hands-on approach to implementing real-world machine learning use cases that will have you up and running quickly.