List of Machine Learning APIs AT&T Speech, IBM Watson, Google Prediction

Published on Author Code Father
List of Machine Learning APIs AT&T Speech, IBM Watson, Google Prediction. Top Machine Learning APIs.

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 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:

  • Popularity
  • Potential
  • Documentation
  • Ease of use
  • Functionality

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, Cogito, DataSift, iSpeech, Microsoft Project Oxford, Mozscape, and OpenCalais.

AT&T Speech

Link: Provider: AT&T API Documentation URL: Demos: 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.


IBM Watson

Link: Provider: IBM API Documentation URL: Demos: 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.


Google Prediction

Link: Provider: Google API Documentation URL: Demo: 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.


Link: Provider: API Documentation URL: Demo: 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 API to add an intelligent voice interface to home automation, connected car, smart TV, robotic, smartphone, wearable, and many other types of applications. The 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. was acquired by Facebook in January. However, according to the announcement post, will remain free and open to all developers.



Link: Provider: AlchemyAPI/IBM API Documentation URL: Demos: 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.



Link: Provider: Diffbot API Documentation URL: Demos: 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. Diffbot provides API documentation that is well organized and easy to follow. More than 35 client libraries including PHP, Python, JavaScript, Objective C, and Perl, are available. In October 2014, the company released the Diffbot Analyze API, which visually analyzes a web page, then determines which Diffbot extraction API should be used. Last month, it was reported that Diffbot has created a knowledge graph rivaling that of Google’s and that Microsoft Bing is using it to automatically generate contextual results.



Link: Provider: BigML API Documentation URL: Models Gallery: Founded in 2011, BigML is a machine learning platform used primarily for predictive modeling. The BigML platform features anomaly detection, cluster analysis, SunBurst visualization for decision trees, text analysis, and more. The BigML API allows applications to access predictive models and other BigML resources. Using the API, applications can perform CRUD operations on BigML resources using standard HTTP methods. BigML provides a nicely designed developer site that features well-organized and comprehensive API documentation, code samples, client libraries, a quick start page, and other developer tools. In February 2014, BigML reached a major milestone: 1 million predictive models created with the BigML platform.



Link: Provider: PredictionIO API Documentation URL: Founded in 2013, PredictionIO is an open source machine learning server that makes it possible to quickly build predictive engines. PredictionIO features a variety of almost-complete engine templates that can be customized for use cases such as recommendation systems, sentiment analysis, document classification, search results ranking, and product ranking.

PredictionIO features an Event Server that can collect and store arbitrary events. Applications can send events to the server via API, and application events can be retrieved or deleted via API. PredictionIO provides a well-organized and comprehensive documentation site that features SDKs, developer guides, demo tutorials, and more. The latest version of PredictionIO (0.9 Series) was released in March and includes several major improvements, such as new engine templates, evaluation metrics, and hyperparameter tuning support.


Microsoft Azure Machine Learning

Link: Provider: Microsoft API Documentation URL: Gallery: Launched in February, Microsoft Azure Machine Learning is a platform designed for processing massive amounts of data and building predictive applications. The Microsoft Azure ML platform provides capabilities such as natural language processing, recommendation engine, pattern recognition, computer vision, and predictive modeling. The Microsoft Azure ML documentation contains a ton of information. However, information for many of the services is spread across different sections of the Azure website (and some information is on the Project Oxford website), making it somewhat hard to follow. There is an Azure Machine Learning Gallerywhere all of the machine learning APIs, experiments, and tutorials are listed in one place. While the Microsoft Azure ML platform is rather new, the service has already gained significant popularity. It will be interesting to see how Microsoft’s machine learning platform fares against Google, IBM, and Amazon in the coming months.


Amazon Machine Learning

Link: Provider: Amazon API Documentation URL: The Amazon Machine Learning platform has gained a lot of popularity in the short time since its launch in April. The service makes it possible to build intelligent applications that feature machine learning capabilities such as pattern recognition and prediction. Developers can use Amazon ML APIs to build applications that feature fraud detection, content personalization, document classification, customer churn prediction, and more. Amazon provides comprehensive, detailed information about the Amazon ML platform and APIs. However, the documentation is somewhat hard to follow, and some of the information is provided in PDF format. The Amazon ML developer site features a large selection of SDKs and client libraries, a forum, an API reference section, machine learning concepts section, and more. The Amazon ML service seems to be a bit more complicated than Google Prediction or Microsoft Azure ML. However, Amazon does provide visualization tools and wizards that help users with the process of creating machine learning models. Both Amazon ML and Microsoft Azure ML are new services that have become popular in a very short amount of time. It will be interesting to see which company, Microsoft or Amazon, will have the larger share of the machine learning cloud services market in the future.