SageMaker Feature Store – Amazon SageMaker Modeling – MLS-C01 Study Guide

SageMaker Feature Store

Imagine you are building a recommendation system. In the absence of Feature Store, you’d navigate a landscape of manual feature engineering, scattered feature storage, and constant vigilance for consistency.

Feature management in an ML pipeline is challenging due to the dispersed nature of feature engineering, involving various teams and tools. Collaboration issues arise when different teams handle different aspects of feature storage, leading to inconsistencies and versioning problems. The dynamic nature of features evolving over time complicates change tracking and ensuring reproducibility. SageMaker Feature Store addresses these challenges by providing a centralized repository for features, enabling seamless sharing, versioning, and consistent access across the ML pipeline, thus simplifying collaboration, enhancing reproducibility, and promoting data consistency.

Now, user data, including age, location, browsing history, and item data such as category and price, have a unified home with Feature Store. Training and inference become a joyride, with easy access and sharing of these features, promoting efficiency and unwavering consistency.

To navigate the terrain of SageMaker Feature Store, let’s familiarize ourselves with some key terms:

• Feature store: At its core, a feature store is the storage and data management layer for ML features. It stands as the single source of truth, handling storage, retrieval, removal, tracking, sharing, discovery, and access control for features.
• Online store: This is the realm of low latency and high availability, allowing real-time lookup of records. The online store ensures quick access to the latest record via the GetRecord API.
• Offline store: When sub-second latency reads are not a priority, the offline store stores historical data in your Amazon S3 bucket. It’s your go-to for storing and serving features for exploration, model training, and batch inference.
• Feature group: The cornerstone of Feature Store, a feature group contains the data and metadata crucial for ML model training or prediction. It logically groups features used to describe records.
• Feature: A property serving as an input for ML model training or prediction. In the Feature Store API, a feature is an attribute of a record.
• Feature definition: Comprising a name and data type (integral, string, or fractional), a feature definition is an integral part of a feature group.
• Record: A collection of values for features tied to a single record identifier. The record identifier and event time values uniquely identify a record within a feature group.
• Record identifier name: Each record within a feature group is defined and identified with a record identifier name. It must refer to one of the names of a feature defined in the feature group’s feature definitions.
• Event time: The time when record events occur is marked with timestamps, which are vital for differentiating records. The online store contains the record corresponding to the latest event time, while the offline store contains all historic records.
• Ingestion: The process of adding new records to a feature group, usually achieved through the PutRecord API.

Let’s combine the tools covered in this chapter so far to navigate through an example of fraud detection in financial transactions. Table 9.2 shows a synthetic dataset for financial transactions:

TransactionID	Amount	Merchant	CardType	IsFraud
1	500.25	Amazon	Visa	0
2	120.50	Walmart	Mastercard	1
3	89.99	Apple	Amex	0
4	300.75	Amazon	Visa	0
5	45.00	Netflix	Mastercard	1

Table 9.2 – Example dataset for financial transactions

You will now see the applications of SageMaker Feature Store, SageMaker Training Compiler, SageMaker Debugger, and SageMaker Model Monitor on the above dataset.

1. Feature engineering with SageMaker Feature Store: You can store transaction (financial transactions, in this example) features intelligently and it ensures consistency across the training and inference stages. Versioning comes into play, offering a timeline of your features’ evolution.
   1. Define the features: Amount, Merchant, CardType
   1. Ingest data into Feature Store: Use the SageMaker Feature Store API to ingest the dataset into Feature Store:

# Example code for ingesting data into Feature Store

from sagemaker.feature_store.feature_group import FeatureGroup

feature_group_name = “financial-transaction-feature-group”

feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=sagemaker_session)

feature_group.load_feature_definitions(data_frame=df)

feature_group.create()

feature_group.ingest(data_frame=df, max_workers=3, wait=True)

• Optimize training with SageMaker Training Compiler: You can utilize SageMaker Training Compiler to optimize and compile training scripts:

# Example code for defining a training job with SageMaker Training Compiler

from sagemaker.compiler import compile_model

compiled_model = compile_model(

    target_instance_family=’ml.m5.large’,

    target_platform_os=’LINUX’,

    sources=[‘train.py’],

    dependencies=[‘requirements.txt’],

    framework=’pytorch’,

    framework_version=’1.8.0′,

    role=’arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20201231T000001′,

    entry_point=’train.py’,

    instance_type=’ml.m5.large’,

)

• Precision debugging with SageMaker Debugger: You can integrate SageMaker Debugger hooks into your training script for real-time monitoring to identify training issues in real time, such as vanishing gradients or model overfitting:

# Example code for integrating SageMaker Debugger in the training script

from smdebug import SaveConfig

from smdebug.pytorch import Hook

# Create an instance of your model

model = FraudDetectionModel(input_size, hidden_size, output_size)

hook = Hook.create_from_json_file()

hook.register_hook(model)

# Your training script here…

# Train the model train_model(model, train_loader, criterion, optimizer, num_epochs=5)

• Model deployment and inference: You can deploy your trained model with SageMaker, tapping into the rich repository of features stored in the Feature Store. Real-time monitoring with SageMaker Model Monitor ensures the model’s health in the dynamic world of inference.
• Continuous monitoring with SageMaker Model Monitor: The story doesn’t end with deployment. SageMaker Model Monitor becomes your sentinel, continuously guarding the deployed model. Detecting concept drift and data quality nuances, it ensures your model remains a reliable guide in the real-world production environment. Use SageMaker Model Monitor to capture baseline statistics for your deployed model:

# Example code for capturing baseline statistics with SageMaker Model Monitor

from sagemaker.model_monitor import DefaultModelMonitor

from sagemaker.model_monitor.dataset_format import DatasetFormat

monitor = DefaultModelMonitor(

    role=role,

    instance_count=1,

    instance_type=’ml.m5.large’,

    volume_size_in_gb=20,

    max_runtime_in_seconds=3600,

)

baseline_data_uri = ‘s3://path/to/baseline_data’

monitor.suggest_baseline(

    baseline_dataset=baseline_data_uri,

    dataset_format=DatasetFormat.csv(header=True),

    output_s3_uri=’s3://path/to/baseline_output’,

)

In the next section, you will learn about Amazon SageMaker Edge Manager, a service provided by AWS to facilitate the deployment and management of ML models on edge devices.