Amazon SageMaker Feature Store: Provisioned Capacity Mode

/ Tutorial

Overview

The Amazon SageMaker Feature Store is a fully managed, purpose-built service that makes it easy to store, discover, and share machine learning (ML) features. It enables you to store features in a centralized and organized manner, making them readily accessible for ML model development, training, and inference.

In the latest release, the Amazon SageMaker Feature Store introduces the provisioned capacity mode, which allows you to specify write and read capacities for feature groups during creation. This revolutionary feature provides enhanced flexibility and cost optimization for managing your feature store.

This comprehensive guide will provide you with in-depth knowledge about the provisioned capacity mode, its benefits, how to use it effectively, and additional technical details to maximize the capabilities of the SageMaker Feature Store.

Table of Contents

  1. Understanding the Provisioned Capacity Mode
    • What is Provisioned Capacity Mode?
    • Benefits of Provisioned Capacity Mode
  2. Getting Started with Amazon SageMaker Feature Store
    • Overview of Amazon SageMaker Feature Store
    • Key Concepts
    • Prerequisites
  3. Creating a Feature Group in Provisioned Capacity Mode
    • Specifying Write and Read Capacities
    • Managing Costs by Adjusting Capacity Limits
    • Switching Between Provisioned Mode and On-Demand Mode
  4. Setting Billing Mode
    • UI Approach
    • Programmatically via APIs and the SDK
  5. Technical Details of Provisioned Capacity Mode
    • Architectural Overview
    • Scalability and Performance Considerations
    • Monitoring and Alerting
  6. Best Practices for Using Provisioned Capacity Mode
    • Capacity Planning
    • Cost Optimization Techniques
    • Handling Dynamic Traffic Patterns
    • Backup and Disaster Recovery
  7. Troubleshooting and FAQs
    • Common Issues and Resolutions
    • Frequently Asked Questions
  8. Conclusion
  9. References

1. Understanding the Provisioned Capacity Mode

What is Provisioned Capacity Mode?

The provisioned capacity mode in the Amazon SageMaker Feature Store is a feature introduced to offer increased control over the read and write capacities for feature groups. With this mode, you can specify the desired provisioned capacity limits during the creation of feature groups. This allows you to allocate resources based on your specific workload requirements and optimize costs.

Previously, the SageMaker Feature Store operated only in on-demand mode, where the capacity was automatically scaled based on the traffic patterns. However, this might not always be ideal for managing costs or accommodating specific workload variations. The introduction of provisioned capacity mode now addresses these challenges by providing the ability to define and adjust capacity limits as needed.

Benefits of Provisioned Capacity Mode

The provisioned capacity mode brings several benefits to Amazon SageMaker Feature Store users, including:

  1. Cost Optimization: By specifying provisioned capacities, you have full control over the resources allocated to each feature group. This allows you to optimize costs by aligning resources with your workload requirements, avoiding overprovisioning, and scaling down during off-peak periods.

  2. Increased Performance: Provisioned capacity mode enables you to allocate ample resources to handle high-throughput read and write workloads. By defining read and write capacities based on your application’s traffic patterns, you can ensure optimal system performance.

  3. Flexibility: The ability to switch between provisioned mode and on-demand mode at the feature group level gives you the flexibility to adapt to changing workload patterns without any disruptions. This ensures that your system can handle varying demands efficiently.

  4. Fine-Grained Control: With provisioned capacity mode, you can fine-tune and adjust capacity limits whenever necessary. This granular control allows you to manage costs, optimize performance, and align resources with specific time-based requirements.

2. Getting Started with Amazon SageMaker Feature Store

Overview of Amazon SageMaker Feature Store

Before diving into provisioned capacity mode, it is essential to understand the basics of the Amazon SageMaker Feature Store.

The Amazon SageMaker Feature Store is a fully managed service that enables you to store, organize, and share ML features. It provides a centralized repository for features, allowing data scientists and ML engineers to access them seamlessly during model development, training, and inference.

Features in the SageMaker Feature Store are stored in feature groups, which are logical collections of features that share the same schema. Feature groups can be thought of as tables in a database, where each row represents a unique record or observation, and each column represents a feature. The Feature Store takes care of managing the storage, indexing, and retrieval of feature data in an optimized manner.

Key Concepts

To fully grasp the concept of provisioned capacity mode, it is important to understand the key concepts of the Amazon SageMaker Feature Store, including:

Feature Group

A feature group is a fundamental unit of organization in the SageMaker Feature Store. It consists of a collection of features that share the same schema. When creating a feature group, you define the name, description, and schema of the group.

Feature

A feature represents an individual data point or attribute that contributes to the training or inference of ML models. Features can be categorical, numerical, timestamp-based, or any other data type required for your application.

Feature Store

The Feature Store is the central repository that stores all the feature groups. It provides a unified interface for storing, querying, and managing feature data.

Feature Store API

The Feature Store API is an interface that allows you to interact with the Feature Store programmatically. It provides methods for creating feature groups, ingesting feature data, querying features, and more.

Prerequisites

Before using the provisioned capacity mode in the Amazon SageMaker Feature Store, you should ensure the following prerequisites are met:

  1. Amazon Web Services (AWS) Account: To access and utilize the SageMaker Feature Store, you need an AWS account. If you do not have an account, sign up for one at https://aws.amazon.com/.

  2. SageMaker Feature Store Enabled: Ensure that the Amazon SageMaker Feature Store is enabled in your AWS account. If it is not currently enabled, you can enable it through the AWS Management Console or by using the AWS Command Line Interface (CLI).

3. Creating a Feature Group in Provisioned Capacity Mode

Once you have a basic understanding of the Amazon SageMaker Feature Store and the prerequisite requirements are met, you can create feature groups in provisioned capacity mode.

Specifying Write and Read Capacities

When creating a feature group in provisioned capacity mode, you have the ability to specify the desired write and read capacities. The write capacity defines the maximum number of write transactions per second that can be made to the feature group, while the read capacity defines the maximum number of read transactions per second.

By setting these capacities appropriately, you can ensure that your feature groups can handle the expected workload efficiently, without being limited by default capacity constraints.

Here is an example of how you can create a feature group with provisioned capacities:

“`markdown

Creating a Feature Group in Provisioned Capacity Mode

To create a feature group in provisioned capacity mode, follow these steps:

  1. Open the Amazon SageMaker console and navigate to the Feature Store.

  2. Click on “Create Feature Group” to start the creation process.

  3. Provide a name and description for the feature group.

  4. Define the schema of the feature group by specifying the feature names and their corresponding data types.

  5. Choose the provisioned capacity mode.

  6. Specify the desired write capacity and read capacity.

  7. Optionally, configure other settings such as encryption and data store.

  8. Click on “Create Feature Group” to create the feature group with the specified provisioned capacities.

By following these steps, you can create a feature group in provisioned capacity mode with the desired capabilities to handle your application’s workload effectively.
“`

Managing Costs by Adjusting Capacity Limits

One of the key advantages of the provisioned capacity mode is the ability to manage costs by adjusting capacity limits. You can change the provisioned capacities at any time, allowing you to scale up or down based on your workload requirements.

For example, you may have higher traffic during weekdays and lower traffic during weekends. By dynamically adjusting the read and write capacities, you can align the provisioned resources with the workload variations and avoid overprovisioning during off-peak periods.

The process of adjusting capacity limits is straightforward and can be done through the Amazon SageMaker console or programmatically using the Feature Store API. The API provides methods to update provisioned capacities for feature groups, making it easy to automate capacity adjustments.

Switching Between Provisioned Mode and On-Demand Mode

Another powerful feature of the provisioned capacity mode is the ability to switch between provisioned mode and on-demand mode at the feature group level. This allows you to adapt to changing workload patterns without any disruptions to your application.

For example, you may start with provisioned capacity mode to handle predictable high traffic periods. However, as traffic patterns change, you may want to switch to on-demand mode to leverage automatic scaling based on usage patterns.

By providing this flexibility, the provisioned capacity mode ensures that you can optimize resources based on your specific requirements at any given time.

4. Setting Billing Mode

Setting the billing mode for the provisioned capacity mode can be done through the Amazon SageMaker console or programmatically using the Feature Store API and the SageMaker SDK. Both approaches are explained below.

UI Approach

To set the billing mode via the Amazon SageMaker console, follow these steps:

  1. Open the Amazon SageMaker console and navigate to the Feature Store.

  2. Click on the feature group for which you want to set the billing mode.

  3. Under the “Settings” tab, find the “Billing Mode” section.

  4. Click on the “Edit” button.

  5. Select the desired billing mode (provisioned or on-demand).

  6. Click on “Save” to apply the changes.

Programmatically via APIs and the SDK

To set the billing mode programmatically, you can use the Feature Store API and the SageMaker SDK. The SDK provides a high-level interface for interacting with the Feature Store API and simplifies the process of updating the billing mode.

Here is an example of how to set the billing mode using the SageMaker SDK:

“`markdown

Setting Billing Mode Programmatically

Using the SageMaker SDK, you can set the billing mode programmatically. Here’s an example of how to do it:

“`python
import boto3
from sagemaker.feature_store.feature_group import FeatureGroup

Create a FeatureGroup object

feature_group = FeatureGroup(name=”my-feature-group”)

Set the billing mode to provisioned

feature_group.set_billing_mode(“Provisioned”)

Print the updated billing mode

print(f”Billing mode: {feature_group.billing_mode}”)
“`

By executing this code, you can set the billing mode to “Provisioned” for your desired feature group programmatically.
“`

5. Technical Details of Provisioned Capacity Mode

To fully leverage the capabilities of the provisioned capacity mode in the Amazon SageMaker Feature Store, it is essential to understand the technical details and considerations.

Architectural Overview

The provisioned capacity mode operates by allocating and managing resources based on the specified capacities for feature groups. Amazon SageMaker decouples read and write workloads, allowing separate provisioned capacities for each.

Under the hood, the Feature Store leverages highly scalable and distributed storage and compute infrastructure to handle the large-scale data requirements of machine learning workloads. This architecture ensures high throughput and low latency access to feature data, even with extensive read and write operations.

Scalability and Performance Considerations

With provisioned capacity mode, you can scale the resources allocated to feature groups based on your requirements. However, it’s important to consider the scalability and performance considerations to ensure optimal system performance.

Scaling Write Workloads

To scale write workloads efficiently, you should monitor the write capacity utilization of feature groups. If the utilization consistently exceeds the provisioned write capacity, it might be an indication that the provisioned capacity needs to be increased.

Scaling Read Workloads

Similarly, monitoring the read capacity utilization is vital to ensure sufficient resources for read operations. If the consumption of read capacity exceeds the provisioned limit frequently, it might be necessary to increase the provisioned capacity for better performance.

You can utilize Amazon CloudWatch metrics and alarms to monitor capacity utilization and trigger automatic scaling actions if required.

Monitoring and Alerting

Monitoring the performance and capacity utilization of feature groups is crucial for maintaining a healthy and efficient system. The Amazon SageMaker Feature Store provides various tools and mechanisms for monitoring and alerting, including:

  • Amazon CloudWatch: Amazon CloudWatch allows you to collect and monitor metrics for feature groups, such as read and write capacity utilization. You can configure custom dashboards, set alarms, and receive notifications when certain thresholds are exceeded.

  • Amazon EventBridge: Amazon EventBridge enables you to create event rules to react to specific events, such as capacity breaches or data ingestion failures. These rules can trigger Lambda functions, sending notifications or executing custom actions.

  • Amazon Simple Notification Service (SNS): SNS can be used to send email, SMS, or other notifications based on the events triggered by CloudWatch or EventBridge.

By leveraging these monitoring and alerting capabilities, you can proactively identify and mitigate performance issues, ensuring smooth operations of your feature store.

6. Best Practices for Using Provisioned Capacity Mode

To effectively utilize the provisioned capacity mode in the Amazon SageMaker Feature Store, it is recommended to follow these best practices:

Capacity Planning

Capacity planning is a critical aspect of managing provisioned capacities. It involves accurately estimating your feature group’s read and write requirements to allocate appropriate resources. Here are some considerations for capacity planning:

  • Workload Analysis: Analyze historical traffic patterns and use ML forecasting techniques to determine peak and off-peak traffic periods. This analysis helps you identify the most appropriate provisioned capacities.

  • Data Ingestion Rate: Understand the rate at which new data is ingested into feature groups. Adjust the write capacity accordingly to handle the ingestion rate efficiently.

  • Data Retrieval Rate: Analyze the read patterns and the number of read requests per second to determine the required read capacity. Ensure that the provisioned read capacity can effectively serve the expected query load.

Cost Optimization Techniques

Optimizing costs is a key consideration when working with provisioned capacities. Some techniques to optimize costs include:

  • Right-Sizing: Regularly analyze your workload and adjust the provisioned capacities based on the observed utilization. Avoid overprovisioning to minimize unnecessary costs.

  • Reserved Capacity: If you have predictable workloads with consistent traffic patterns, consider utilizing Reserved Instances or Savings Plans to further reduce costs. This applies to instances used for data ingestion and feature data retrieval.

Handling Dynamic Traffic Patterns

Workload patterns can change due to various factors, such as seasonality or marketing campaigns. To handle dynamic traffic patterns effectively, consider these strategies:

  • Auto-Scaling: In addition to provisioned capacities, consider utilizing auto-scaling for both read and write capacities. This approach allows the system to automatically adjust resources based on observed traffic patterns.

  • Redundancy and Caching: Employ caching techniques to reduce the dependency on read operations from the Feature Store. This can be achieved by leveraging in-memory databases or utilizing additional data stores for frequently accessed data.

Backup and Disaster Recovery

Data integrity and availability are critical for machine learning workflows. Ensure you have appropriate backup and disaster recovery mechanisms in place to mitigate potential data loss. Some best practices include:

  • Cross-Region Replication: Enable replication of your feature group data across multiple AWS regions for disaster recovery purposes. This ensures that even in the event of regional outages, your data remains accessible.

  • Data Versioning: Maintain versions of your data to track changes and enable rollbacks if necessary. This ensures that you can recover to a known good state if there are issues with the current data.

7. Troubleshooting and FAQs

Common Issues and Resolutions

  1. Issue: Provisioned capacities are not being fully utilized.

  2. Resolution: Check if there are any bottlenecks in the system, such as data ingestion or data retrieval processes. Addressing these bottlenecks can help in effectively utilizing provisioned capacities.

  3. Issue: Increase in latency of read or write operations.

  4. Resolution: Analyze the system performance and resource utilization metrics to identify potential issues. Scaling up the provisioned capacities or optimizing your application code can help alleviate latency issues.

Frequently Asked Questions

  1. Question: Can I dynamically adjust provisioned capacities without any downtime?

  2. Answer: Yes, provisioned capacities can be adjusted dynamically without any downtime. The SageMaker Feature Store ensures seamless transitions when increasing or decreasing capacities.

  3. Question: Can I switch between provisioned mode and on-demand mode for individual feature groups?

  4. Answer: Yes, you have the flexibility to switch between provisioned mode and on-demand mode at the feature group level. This allows you to adapt to varying workload requirements for specific feature groups.

8. Conclusion

The provisioned capacity mode in the Amazon SageMaker Feature Store offers enhanced control, flexibility, and cost optimization for managing your ML feature storage. By understanding how to create feature groups, specify provisioned capacities, and adjust capacity limits dynamically, you can fully leverage the capabilities of the provisioned capacity mode.

In this comprehensive guide, we covered the basics of the SageMaker Feature Store, the benefits of provisioned capacity mode, how to create feature groups, and technical considerations. We also explored best practices to optimize costs, handle dynamic workload patterns, and ensure data integrity.

By following the best practices outlined in this guide, you can effectively utilize the provisioned capacity mode and further enhance your ML workflows using the Amazon SageMaker Feature Store.

9. References