By performing differential computing to turn API data into event-driven streams, Streamdata.io helps data scientists take advantage of API data in an easy fashion so they can build, test, and optimize their machine learning models.
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.
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.
As a serverless streaming proxy, Streamdata.io 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 Streamdata.io's proxy is also an event that can be integrated into a machine learning platform – setting you up for stream processing success.
Less data to transfer, one-way latency, and streaming increases predictions efficiency.
HTTPS and TLS protect the integrity of any data that is exchanged through Streamdata.io.
With no server-side code, you can easily create an event feed in minutes from any REST API.