Machine learning technology, which is defined in this ProgrammableWeb article, is starting to become a common component in many types of technology platforms. Machine learning has led to the advent of predictive analytics, which has been rapidly growing in usage and is becoming an important aspect of business operations and processes.
Predictive analytics is already being used in many common online activities, such as the Google search box auto suggest and Amazon.com product recommendations. Predictive analytics was even used by the Obama for America organization in the operation and management of its presidential election campaigns. Senior members of the Obama for America analytics team recently founded their own company, BlueLabs, which specializes in predictive analytics and data science. Company services include predictive modeling, analytics infrastructure and media optimization.
Recent reports by leading IT research companies Gartner and Transparency Market Research have indicated that the global predictive analytics market will continue to see steady growth in the next few years. The number of companies and industries that utilize predictive analytics will increase substantially in 2014 and will continue to see dramatic growth.
Predictive analytics provides benefits to a variety of industries and real-world applications including sales, marketing, finance, risk management, business operations and more. Industry-specific predictive analytics software solutions are being developed, including customer intelligence and personalization, fraud and systems security, financial lending and risk management, campaign management, business operations, and many others.
A recent report by Transparency Market Research forecasts the global predictive analytics market to reach $6.5 billion (USD) by 2019. From 2013 to 2019, the market is expected to grow at approximately 17.8% compound annual growth rate.
In addition, 70% of the most profitable companies will manage business processes using real-time predictive analytics or extreme collaboration by 2016, according to a report by Gartner. In another recent Gartner report, Gartner Research Analyst Samantha Searle mentions the importance of predictive metrics to businesses:
Using historical measures to gauge business and process performance is a thing of the past. To prevail in challenging market conditions, businesses need predictive metrics—also known as “leading indicators”—rather than just historical metrics (a.k.a. “lagging indicators”). Predictive risk metrics are particularly important for mitigating and even preventing the impact of disruptive events on profitability.
Machine learning and predictive analytics have been increasingly making the news. In June 2013, Walmart Labs announced the acquisition of Inkiru, a company that specializes in predictive intelligence technology. The acquisition of Inkiru by Walmart Labs will allow Walmart to use the Inkiru predictive analytics platform to provide greater site personalization and search capabilities as well as improved fraud prevention and marketing.
Last month, Apigee acquired InsightsOne, a predictive analytics applications developer. The acquisition of InsightsOne allows Apigee to incorporate big data predictive analytics into the company’s digital business platform. Waqar Hasan, InsightsOne CEO, will be joining the Apigee executive team. He states in the press release:
Predictive is the ‘killer app’ for big data, and Apigee is the only company that delivers predictive analytics with API and app infrastructure in an integrated platform. InsightsOne is a natural fit for Apigee, expanding its big data analytics to make all customer interactions smarter and more effective.
Earlier this month, Yahoo and Carnegie Mellon University announced a five-year, $10 million partnership to research and test machine learning technologies with real-time data from Yahoo. The main goal of the project is to determine if machine learning and interface technologies can provide improved personalized user experiences. The partnership has been named “Project InMind” and will be supported financially by Yahoo on a yearly basis. The project will feature a new fellowship program at CMU, also sponsored by Yahoo.
Companies that develop machine learning and predictive technologies are becoming far more common. Below are a few examples of machine learning and/or predictive analytics platforms.
These companies were chosen to show a sampling of the market and also because they all provide APIs for developers.
BigML is a cloud-based machine learning platform that allows users to create visual predictive models using raw data and structured datasets. Last month, BigML announced the availability of the 2014 winter release, which includes features that boost predictive modeling. The company also introduced a new paradigm called Programmatic Machine Learning that is the “ability to programmatically transform a dataset via a high-level language and a cloud-based API together.”
The BigML API makes it possible for developers to build applications that incorporate predictive models and near real-time predictions.
Datumbox is a machine learning platform that focuses on natural language processing (NLP). The Datumbox platform features a variety of functions including sentiment analysis, Twitter sentiment analysis, language detection, educational detection and keyword extraction.
The Datumbox API provides programmatic access to the platform’s natural language processing and text analysis functions. ProgrammableWeb recently published an article featuring the Datumbox API.
Diffbot uses computer vision, machine learning and other technologies to extract text, images, links, HTML attributes and other elements from Web pages. In August 2013, the company released the Diffbot Product API, which can extract product information from the pages of e-commerce websites. Earlier this month, ProgrammableWeb reported on the release of 35+ new Diffbot client libraries in a variety of programming languages.
The company provides a suite of Diffbot APIs for extracting data from Web page news articles, Web site home pages, e-commerce product pages and other types of Web pages. There are also APIs for extracting Web page images and automatically classifying Web page links.
Ersatz Labs is a startup and developer of a new platform called Ersatz, described by the company as “the first cloud-based neural network platform.” The Ersatz platform allows developers to build applications that utilize deep neural networks without the need to have extensive knowledge in machine learning.
There is an API that can be accessed via HTTP, and a client library in Python is also available, so Ersatz can be easily integrated with Web, mobile and desktop applications. Ersatz is currently in private beta, and developers interested in participating can request an invitation on the official company Web site.
The Google Prediction API provides developers access to Google’s cloud-based machine learning platform and pattern-matching functions. The API is used in conjunction with the Google Cloud Storage API and allows developers to incorporate functions into their apps such as sentiment analysis, spam detection, message routing decisions, suspicious activity identification and more.
IBM Watson is a machine learning platform that focuses on NLP, hypothesis generation and evidence-based learning. In November 2013, ProgrammableWeb reported that IBM had launched the Watson Developer Cloud, a cloud-based marketplace that provides access to APIs, documentation, self-service training materials and other tools for developers to build IBM Watson-powered applications.
Last month, IBM announced that the company will invest more than $1 billion in the new Watson Group, which will be based in New York City’s “Silicon Alley.” The new group will focus on developing and promoting the IBM Watson platform and cognitive technologies. IBM also announced new Watson cognitive intelligence-based services, including IBM Watson Discovery Advisor, IBM Watson Analytics and IBM Watson Explorer.
Logical Glue is a machine learning as-a-service (MLaaS) platform that features predictive model building, predictive model real-time deployment, and real-time predictive analytics. The platform is designed to predict customer behavior for many types of markets, particularly financial lending, insurance and marketing.
The Logical Glue platform is currently in private beta; however, companies can apply to participate in the beta program, which allows them access to the platform prerelease. The next release of the platform will include the Logical Glue prediction API.
Parse.ly is a predictive content optimization and analytics platform designed for blogs, news sites and other online publishers. The home page of the Parse.ly website describes the company as “The Content Performance Authority” and the platform provides users a real-time view of article traffic based on individual posts, authors, sections and referrers. The Parse.ly platform also provides views of content metrics, social network shares, site activity and other analytics.
The Parse.ly API allows developers to programmatically access platform features such as analytics, shares, referrers, real-time, search and recommendations. There are also mobile SDKs available that can be integrated into third-party apps so reader activity can be tracked.
PredictionIO is a machine learning server that allows developers to add predictive features to software, web and mobile applications. PredictionIO is open source and can be installed on a stand-alone server. There is also a cloud version available on Amazon EC2/Amazon EBS.
The PredictionIO API enables applications to collect and manage app data and add predictive features such as predict user preferences, personalized content, content discovery, content recommendations and more. ProgrammableWeb recently published an interview with Simon Chan (cofounder and CEO of PredictionIO), which covers PredictionIO features, compares other machine learning APIs and more.
SwiftKey
SwiftKey is a developer of touchscreen keyboard applications and word prediction technology. SwiftKey’s products Keyboard, Flow and Note incorporate machine learning and SwiftKey’s language technology, available to developers via API and SDK.
A recent TechCrunch article featured SwiftKey’s word prediction technology. Nathan Matias, a PhD student at the MIT Media Lab, used SwiftKey technology to create a sonnet essentially co-authored by Shakespeare and generated entirely from the SwiftKey next word suggestions.
Predictive Analytics World
The rapidly growing predictive analytics market has led to the creation of Predictive Analytics World, a regularly occurring event that focuses on the business applications of predictive analytics such as marketing, CRM, product recommendation systems, credit scoring, fraud detection and more.
There are Predictive Analytics World events throughout 2014 in cities including San Francisco (March 16-21), Toronto (May 12-15), Chicago (June 16-19), Boston (October 5-9), London (October 29-30) and Berlin (November 4-5).
Conclusion
New and disruptive technologies are being invented and developed at an extremely rapid pace. Companies that are not using technology that has become necessary to business operations will most likely fall far behind their competitors.
Machine learning, predictive analytics and APIs for that matter are not technologies of the future, but important technologies of the present. For businesses to succeed in this day and age, “having a website” is simply not enough. Companies need to adopt new technologies and modern day business models or they will be left behind.
By Janet Wagner. Janet is a data journalist and full stack developer based in Toledo, Ohio. Her focus revolves around APIs, open data, data visualization and data-driven journalism. Follow her on Twitter, Google+, and LinkedIn.
Originally published at blog.programmableweb.com
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