Use Event-Driven Streaming to Enhance Your Machine Learning Models

By performing differential computing to turn API data into event-driven streams, helps data scientists take advantage of API data in an easy fashion so they can build, test, and optimize their machine learning models.

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The Current Problem

API Polling Fails to Deliver Event-Driven Data

Machine learning algorithms rely on events, while the request/response mechanism of API polling is not event-oriented. Because of this, simple API polling cannot be used to feed machine learning models unless it has already been processed to remove redundant data. In addition, advanced ML algorithms and models fail to predict and deliver efficiently without being updated with events as they happen.

Batch Processing is Inefficient

Machine learning projects using API data usually end up relying on batch processing – a method by which a lot of data is uploaded from time to time. But it takes an excessively long time to digest these large clusters of data, causing ML models to lose their efficiency between batches.

The Solution

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Why is this better?

As a serverless streaming proxy, turns any request/response REST API into an event-driven streaming API. By performing differential computing on API calls and only sending new data as it appears, each message received from's proxy is also an event that can be integrated into a machine learning platform – setting you up for stream processing success.

Additional Benefits

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Less data to transfer, one-way latency, and streaming increases predictions efficiency.

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HTTPS and TLS protect the integrity of any data that is exchanged through

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With no server-side code, you can easily create an event feed in minutes from any REST API.

Discover how helps Data Providers and improves user experiences on Devices & Desktop. is transforming the way data scientists develop machine learning models.

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