As the world evolves, so too do the datasets and features utilized by businesses and customers to train their models. To maintain accuracy, it is essential to continually retrain these models in response to changing data. An agile and dynamic methodology is necessary to keep these models updated and responsive to new inputs. This continuous evolution of models, combined with effective retraining, is central to a successful machine learning (ML) strategy.
We are thrilled to introduce the Amazon Comprehend flywheel—a comprehensive feature for machine learning operations (MLOps) designed specifically for Amazon Comprehend models. This post will guide you through creating an end-to-end workflow using the Amazon Comprehend flywheel.
Overview of the Solution
Amazon Comprehend is a fully managed service that employs natural language processing (NLP) to derive insights from document content. It allows users to extract valuable information such as sentiments, key phrases, entities, and more, enabling the use of cutting-edge models tailored to specific applications.
MLOps represents the intersection of data science and engineering, coupled with established DevOps practices, to streamline model deployment throughout the ML development lifecycle. This discipline integrates software development, operations, data engineering, and data science.
The Amazon Comprehend flywheel serves as a centralized hub for executing MLOps with your Amazon Comprehend models. This innovative feature facilitates the maintenance and improvement of your models, allowing for rapid deployment of the most effective versions.
The diagram below illustrates the lifecycle of a model within the Amazon Comprehend flywheel.
Traditionally, creating a new model requires a series of steps. You begin by gathering data and preparing your dataset, followed by training the model. Once trained, the model is evaluated for accuracy before being deployed to an endpoint for inference. With each new model iteration, these processes must be repeated, and manual updates to the endpoint are necessary.
The Amazon Comprehend flywheel automates the entire ML process—from data ingestion to production deployment. This feature enables you to manage training and testing of created models within Amazon Comprehend and automates model retraining when new datasets are ingested into the flywheel’s data lake.
The flywheel integrates seamlessly with custom classification and entity recognition APIs, empowering various roles, including data engineers and developers, to automate and oversee the NLP workflow using no-code services.
Key Concepts
Let’s clarify some key concepts:
- Flywheel: An AWS resource orchestrating the continuous training of a model for custom classification or entity recognition.
- Dataset: A collection of training or testing data used in a flywheel; it guides the training and evaluation of new model versions.
- Data Lake: A designated location in your Amazon Simple Storage Service (Amazon S3) bucket that stores all datasets and model artifacts related to the flywheel.
- Flywheel Iteration: A run of the flywheel initiated by the user, which may involve training new models or assessing the performance of existing ones.
- Active Model: The version selected for predictions; as performance improves, you can update this to the best-performing iteration.
Flywheel Workflow Steps
The following steps outline the flywheel workflow:
- Create a Flywheel: Set up the flywheel to automate training for a custom classifier or entity recognizer, specifying the data lake location.
- Data Ingestion: Create training or testing datasets within the flywheel, managing them in its dedicated data lake.
- Train and Evaluate the Model: Depending on the availability of new datasets, the flywheel will either create a new model or assess the performance of the current one.
- Promote New Active Model Version: Update the active model based on the best performance from various iterations.
- Deploy an Endpoint: Use the flywheel ARN to run real-time inference with the currently active model, automatically updating as new iterations occur.
In the upcoming sections, we will explore different methods for creating a new Amazon Comprehend flywheel.
Prerequisites
To get started, you will need:
- An active AWS account
- An S3 bucket for data storage
- An AWS Identity and Access Management (IAM) role with permissions to create an Amazon Comprehend flywheel and access your S3 bucket
Creating a Flywheel with AWS CloudFormation
To utilize an Amazon Comprehend flywheel via AWS CloudFormation, you must gather specific information about the AWS::Comprehend::Flywheel resource, including the DataAccessRoleArn, DataLakeS3Uri, and FlywheelName. For additional guidance, check out this blog post for further insights.
Creating a Flywheel on the Amazon Comprehend Console
In this example, we will demonstrate how to set up a flywheel for a custom classifier model on the Amazon Comprehend console, designed to identify news topics.
Create a Dataset
First, you need to create a dataset that reflects the data you want to use for training.
This comprehensive overview of the Amazon Comprehend flywheel highlights its capabilities and potential for enhancing your MLOps approach. For expert insights, visit this authority on the subject. If you’re interested in learning more about personal experiences, you can find excellent resources on Reddit related to the Amazon Fulfillment Center.
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