Machine learning is everywhere these days. It's on your smartphone automatically classifying and organizing your photos. It's in your email account filtering out spam and other emails you don't want to read. It's on Amazon.com recommending products and personalizing your online shopping experience. It's in your connected car helping the voice-controlled interface understand you.
Right now, Amazon, Google, IBM, and Microsoft are the biggest players battling to dominate the very fast-growing machine learning cloud services market. IBM further strengthened its position in the market with the recent acquisition of AlchemyAPI, a leading deep learning-based machine learning services platform. Only time will tell which of these companies will succeed in capturing the lion's share of the machine learning cloud services market.
The APIs that made it to our top 10 machine learning APIs list offer a wide range of capabilities including image tagging, face recognition, document classification, speech recognition, predictive modeling, sentiment analysis, and pattern recognition. The APIs also scored well against a diverse set of criteria:
- Ease of use
API popularity is determined using a variety of metrics including ProgrammableWeb followers, GitHub activity, Twitter activity, and search engine popularity based on Google Trends.
Many machine learning APIs that, while popular, did not quite have the metrics to make it into the top 10 machine learning APIs list. Just a few of the APIs worth mentioning are api.ai, Cogito, DataSift, iSpeech, Microsoft Project Oxford, Mozscape, and OpenCalais.
API Documentation URL: http://developer.att.com/apis/speech
Released in 2012, the AT&T Speech API allows developers to add speech-recognition capabilities to web and mobile applications. The AT&T speech API is powered by the AT&T Watson speech engine (no relation to IBM Watson), a speech recognition and natural language understanding platform. Natural language processing is an application of machine learning and NLP includes tasks such as natural language understanding, speech recognition, speech transcription, and many more.
The AT&T Speech API actually consists of three APIs: Speech To Text, Speech To Text Custom, and Text To Speech. The Speech To Text API uses a global dictionary for grammar and is able to transcribe audio data into text based upon the contexts. The Speech To Text Custom API also transcribes audio data into text. However, the transcription is based on grammar or hints specified by the developer. The Text To Speech API is capable of converting text into audio formats such as AMR and WAV.
AT&T provides a nicely designed developer site with well-organized API documentation, demo apps, SDKs, plug-ins, forums, and more. The company regularly organizes hackathons to encourage developers to use AT&T APIs, which include Speech, In-App Messaging, Address Book, and Device Capabilities.
API Documentation URL: https://developer.ibm.com/watson/
One of the most well-known platforms utilizing machine learning along with cognitive computing is IBM Watson. The IBM Watson Developer Cloud, launched in November 2013, offers a suite of APIs (general availability, beta, and experimental) that allow developers to build applications that utilize machine learning technologies such as natural language processing, computer vision, and prediction. The IBM Watson Developer Cloud suite of APIs includes speech to text, text to speech, trade-off analytics, personality insights, question and answer, tone analyzer, and visual recognition.
The IBM Watson Developer Cloud site features comprehensive API documentation, interactive API documentation (Swagger), SDKs, demos, app gallery, forum, content marketplace, and more. IBM plans on continuing to expand Watson Developer Cloud APIs, the Watson Content Marketplace, and commercial partnerships in order to advance the adoption of Watson technologies around the world.
API Documentation URL: https://cloud.google.com/prediction/docs
The Google Prediction API provides access to cloud-based machine learning capabilities including natural language processing, recommendation engine, pattern recognition, and prediction. Developers can use the API to build applications capable of performing sentiment analysis, spam detection, document classification, purchase prediction, and more.
The Google Prediction API documentation is pretty basic and includes code samples, client libraries, a getting started page, and a developer's guide. While the Google Prediction API is one of the most popular machine learning APIs, it should be noted that the latest version (1.6) was released back in June 2013. In October 2014, Google announced the launch of a Smart Autofill Add-on for Google Sheets that uses the Google Prediction API. Other than this news, there does not appear to be much in the way of development when it comes to the Google Prediction API.
Wit.ai is a popular natural language processing platform that makes it possible for developers to add intelligent speech functionality to web and mobile applications. Developers can use the Wit.ai API to add an intelligent voice interface to home automation, connected car, smart TV, robotic, smartphone, wearable, and many other types of applications.
The Wit.ai documentation section is nicely designed, well organized and comprehensive. The API documentation features code samples, SDKs for many popular languages and platforms, quick start guides, and a complete Wit app guide. Wit.ai was acquired by Facebook in January. However, according to the announcement post, Wit.ai will remain free and open to all developers.
API Documentation URL: http://www.alchemyapi.com/developers
AlchemyAPI, an IBM company, provides a suite of deep learning-based cloud services that include AlchemyLanguage, AlchemyVision, and AlchemyData News API. AlchemyAPI provides more than a dozen APIs that developers can use to add machine learning-powered features to applications such as sentiment analysis, entity extraction, concept tagging, image tagging, and facial detection/recognition.
AlchemyAPI provides nicely designed, comprehensive API documentation that includes code samples, SDKs, demos, and a getting started page. AlchemyAPI has been working hard on adding new APIs and features to the platform, and more new features are coming soon. Earlier this month, the company announced a Blockspring-AlchemyAPI integration, making it possible for Blockspring users to leverage AlchemyAPI capabilities without having to write code. In May, AlchemyAPI/IBM announced the launch of the AlchemyData News API, which provides access to an AI-enriched, curated data set of news and blog content.
API Documentation URL: https://www.diffbot.com/dev/docs/
The Diffbot platform utilizes a combination of AI, computer vision, machine learning, and natural language processing to automatically extract data from web pages such as text, images, video, product information, and comments. Diffbot provides a suite of automatic APIs for extracting different types of data from web pages as well as custom APIs that allow data to be extracted using manual rules. Diffbot's Automatic APIs leverage AI to extract clean, structured data without requiring manual rules or training.