Machine learning is a type of artificial intelligence whereby machines take data and interpret it, find patterns, and make predictive decisions based on what they can deduce from the information extracted.
A machine learning API taps into data and pulls predictive answers, which are then sent to the user’s application. Thus, the API is the intermediary between the question and the solution.
The most prominent cloud-based machine learning solutions are Amazon Web Services (AWS), Google, IBM, Microsoft, and Salesforce. One may think that a machine learning API is more complicated than other types of APIs; however, many are built using the REpresentation State Transfer (REST) architecture type. First, developers connect their chosen service’s API with their application. The API then becomes the highway linking the questions posed by the application’s users to the learned answers provided by the server on the other end.
The use cases for machine learning are limited only to imagination, as are their APIs. Some real-world applications include:
Even people who know nothing about this tech have likely benefited from it, such as translating a phrase into another language using Google or taking a picture of a statue to learn more about it or buy a figurine of it.
Machine learning has evolved rapidly over the past ten years and continues to do so. The technology is behind everything from AI chatbots in customer service to self-driving cars. The APIs help power unique applications that bring the power of machine learning into consumer’s hands, allow companies to monitor every portion of their business, and much more.
The power of machine learning is vast, and the use cases are many. Therefore, developers should ensure that the API they choose is connected securely to the correct machine serving up the data predictions they require for their app.
All Machine Learning APIs are supported and made available in multiple developer programming languages and SDKs including:
Just select your preference from any API endpoints page.
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