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API and Machine learning API

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As you can imagine, APIs play many important roles in cloud computing. APIs make the platform extensible which can lead to a rich feature set. They also speed up the platform access and direct a more efficient management of platform security. They also help co-opt with other service providers. APIs ensure compliance and help with successful integration and interoperability. They make handling analytics an easy task.

“API stands for application programming interface through which softwares can interact with each other.”

an application programming interface(API) is a particular set of rules (‘code’) and specifications that software programs can follow to communicate with each other.

it serves as an interface between different software programs and facilities their interaction,similar to the way the user interface facilities interaction between humans and computers.

in another way, an API is the interface through which you access someone elses code or through which someone else’s code accesses yours. in effect the public methods and properties.API is used when 2 or more separate systems need to work together to achieve something they can’t do alone.

API refers to any black box that provides an interface to many underlying functionalities. If you consider network programming, the TCP/IP network stack provides the Sockets API which helps you connect to an external host without dealing with the complex details of networking architecture. You don’t need to worry about the reliability, ordering, app delivery, error detection and so many things.

Whey they use Machine learning API

     Machine Learning APIs make machine learning easy to use, for everyone. Machine Learning APIs         abstract the complexities involved in creating and deploying machine learning models so that                 developers can focus on data munging, user experience, design, experimenting and delivering                  insights from data.


        Machine Learning APIs make it easy for developers to apply machine learning to a dataset so as          to add predictive features to their applications. Machine Learning APIs provide an abstraction layer           for developers to integrate machine learning in real world applications without having to worry               about scaling the algorithms on their infrastructure and getting into the details of the machine                 learning algorithms.


Download Machine learning for Everyone pdf here

What are some great APIs for machine learning

  1. Scikit-learn[1] is probably the best ML library. It has a huge number of features for data mining and data analysis, making it a top choice for researches and developers alike. Its built on top of the popular NumPy, SciPy, and matplotlib libraries, so it'll have a familiar feel to it for the many people that already use these libraries.

      2. IBM Watson

          For Machine Learning practitioners who are crunching at the moment to use IBM Watson’s                               machine intelligence service, within their mobile or web applications, need gnaw no longer.

     3. Microsoft Azure Machine Learning API

      Azure Machine Learning API helps data scientists publish in minutes which once used to take days after          they had developed a feasible model. Azure Machine Learning makes it easy for data scientists to use               predictive models in IoT applications by providing APIs for fraud detection, text analytics,                                 recommendation systems and several other business scenarios

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