There are many companies like Google, IBM, Amazon, and Microsoft helping businesses process big data by building Machine Learning APIs so that organizations can make the best use of the machine learning technology. Machine Learning is the big frontier in big data innovation but it is daunting for people who are not tech geeks or data science domain experts.Similar to how standard APIs help developers create applications, Machine Learning APIs make machine learning easy to use, for everyone. Machine Learning APIs provide businesses with the ability to bring together predictive analytics so that they can get to know their customers better, understand their requirements and deliver products or services based on the past data trends, thereby initiating the selling process.There is an increasing percentage of real time consumer interactions through Machine Learning APIs – making them an ideal option for exposing real time predictive analytics to app developers. 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.

For Deep Knowledge Ventures, the Hong Kong-based venture firm that added a machine learning algorithm named VITAL to its board in 2014, it was about adding a tool to analyse market data around investment opportunities. For global professional service firms experimenting in this space, machine learning could allow deeper and faster document analysis. And though you may not think you are competing with Silicon Valley salaries for talent, you are if you want great people: a great data scientist can easily be 50 times more valuable than a competent one, which means that both hiring and retaining them can be pricey. As the machine learning ecosystem evolves, companies will find interesting ways to combine in-house industry experience with a range of off-the-shelf tools and open source algorithms to create highly-customised decision-support tools.

Watson APIs are now available for public use, albeit only through IBM's Bluemix cloud services platform. IBM's Watson Developer Cloud now offers eight services for building what IBM describes as cognitive apps, with more services promised later on. The Relationship Extraction system seems less limited by available data than Machine Translation, but it is limited in different ways. When the Relationship Extraction system is fed the sentence "Nick Cave's new film '20,000 Days on Earth' debuted yesterday," it understood that "Nick Cave" was a person and that "yesterday" was a date, but didn't understand that "20,000 Days" referred to the title of a work.

Google today announced the public beta launch of its Cloud Natural Language API, a new service that gives developers access to Google-powered sentiment analysis, entity recognition, and syntax analysis. This new API joins Google's other pre-trained machine-learning APIs like the Cloud Speech API, which is now also available in public beta, the Vision API and the Translate API. The new Cloud Natural Language API currently supports texts in English, Spanish and Japanese. Google notes that the idea here is to offer a service "that can meet the scale and performance needs of developers and enterprises in a broad range of industries." Offering an API for sentiment analysis and entity recognition isn't new, of course.

According to Okta CEO Todd McKinnon, there's a lot of hype around the potential of machine learning, but companies aren't actually taking advantage of it. It's similar to how people discussed big data a few years ago. In his view, tech companies need to create and sell intelligent services that let other businesses use machine learning to perform key tasks. Microsoft's data chief, Joseph Sirosh, has said he expects to see a marketplace of intelligent algorithms and applications that companies can buy.