How to Automate Streaming Data

By Media Entertainment Tech Outlook | Wednesday, February 17, 2021

How to Automate Streaming Data

The solution used for large-scale ML business procedures can enhance the journey of carrying on-site machine learning (ML) models to the cloud. 

FREMONT, CA : Carrying on-site machine learning (ML) models to the cloud rapidly in today's environment is an essential part of every cloud migration journey. Here is a description of launching a solution for large-scale ML business processes that improves the migration journey. The solution was developed for clients with streaming data applications (like predictive maintenance, fleet management, and autonomous driving) by the Amazon ML Solutions Lab.

The AWS services utilized in this approach are Amazon SageMaker, a fully managed service that allows a developer and data scientist to design rapidly, train, deploy ML models, and Amazon Kinesis assist with real-time data intake at scale.

When an ML model drifts, it is crucial to automatically refresh ML models with new data to be of high value to any organization. The Amazon SageMaker Model Monitor continuously tracks the performance of Amazon SageMaker ML models in development. It helps them to set warnings for when discrepancies in the consistency of the model arise. The solution provided offers a model refresh architecture released with one-click through an AWS CloudFormation prototype and allows on-the-fly abilities.

Companies can easily connect the real-time streaming data through Kinesis, hold the data on Amazon Redshift, schedule training, and install ML models utilizing Amazon EventBridge. They can even organize AWS Step Function jobs, take the benefits of AutoML abilities through AutoGluon during model training, and get real-time inference from the frequently updated models. In a couple of moments, all this will be available for the companies. The required AWS resources are generated, configured, and linked by the CloudFormation stack.

Solution overview

Here is the solution architecture, which includes three components that are entirely integrated:

Data ingestion – Enables the ingestion of real-time data from either an IoT computer or user-uploaded data and storage of real-time data on a data lake. This feature is specifically designed for circumstances in which vast quantities of real-time data on a data lake need to be processed and organized.

Scheduled model refresh – Offers data stored on a data lake to plan and orchestrate ML workflows. It even helps to train and deploy using AutoML abilities.

Real-time model inference – It helped the trained model and applied in the previous stage to obtain real-time predictions.

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