The Rise of Predictive APIs and Applications in a Digital Era.
By Ali Syed
Throughout the world, organizations and leaders widely acknowledge that digital technologies are transformational. Most of them understand the point that we are not living in the digital revolution. We are living and breathing the history that many will look back at in generations to come and see this time in history as one that fundamentally changed everything. Forever. The society is changing, technology is changing, business models are changing, the way we interact and connect with each other is changing and it’s no longer about becoming digital or not, it’s about sustaining, succeeding and competing in a digital era.
In this digital era organizations can no longer operate, sustain and compete using traditional industrial age thinking, systems, models, technologies and operating frameworks. Digital technologies are irrevocably changing the way organizations and institutions engage and interact with people, citizens, consumers and many other stakeholders. Traditional thinking, operating models, and value delivery channels are being disrupted, driving leaders and executives to reassess their strategies and plans. The combination of dealing with the complexities of the volatile digital world, data deluge, and the pressing need to stay competitive and relevant has sharpened the focus on using digital technologies to transform, in order to stay relevant and competitive.
“Digital technologies are assuming an increasingly prominent place in everyday life, both in the more traditional areas and in the field of new information and communication technologies. Digital is the common language for information, whether in the form of text, pictures or video images. Digitisation, ie the conversion of information into a string of 0s and 1s, provides a common denominator for telephone, television, radio, camera, camcorder or computer signals.”- Information society and a digital world. Report by Council of Europe, Committee on Science and Technology
Data as a fabric of the digital age underpins everything we do. It’s part and parcel of our digital existence. To succeed in a digital world we must master the art and science of managing, leveraging and applying data. Organizations need to become connected and data driven. To sustain and remain competitive they should know what is happening now, what is likely to happen next, and what actions should be taken to get the optimal results. The buzz word associated with this is ‘Big Data’. The trick however is to ignore the Big and just focus on the Data.
Data has become the new raw material: an economic input almost on a par with capital and labor. Organizations need data from multiple systems to make decisions. They need information in easy to read and consistent format to enable fast understanding and response. The path to sustainable and meaningful advantage is being able to find new ways of managing data, discovering what’s in it, finding patterns and predictions, and deciding what to do with all that. Fuelled by data deluge, predictive models and machine learning programs are being used to improve everything around us from the way we shop to the web experiences we enjoy, and the way we receive social and health care.
At the core of any digital transformation is the ability to think creatively and differently around digital technologies, based on the right mix of people, architectures, systems, processes, frameworks and experience around collaboration. This involves creating a mind-set change. One of the areas where we have to do it on immediate basis is how organizations manage, process, and analyze data. Within organizations in every industry, in every part of the world, business and technology leaders are assessing how to get true value from the monolithic amounts of data they already have within and outside their organizations. At the same time, new technologies, comprising of sensors and devices are collecting more data than ever before.
One of the exciting changes in predictive analytics and machine learning during the last 3 years has been the growth of predictive APIs, applications and machine learning as a service (MLaaS) for analyzing and predicting from data. This is an emerging domain. Machine learning platforms of various sorts are revolutionizing many areas of business, public and social services, and predictive APIs (PAPIs) have the potential to bring these capabilities to an even wider range of applications.
Unless you’re a nerd or a developer, you’ve probably never paid much attention to the term “API” — an acronym for “Application Programming Interface.” However, if you’re obsessive compulsive user of social media platforms, you’ve most likely used an application or service built using an API. Twitter, Facebook, LinkedIn, WordPress, WhatsApp, Uber, Amazon, Airbnb and thousands of other applications rely on APIs. What’s more, without APIs, Apple App Store and the Android Marketplace would be very small!
APIs are carefully thought out pieces of code created by programmers for their applications that allow other applications to interact with their application and platform. APIs matter to all organizations operating in the digital era because using them, they can develop platforms, applications and experiences that help us do our jobs effectively and optimally, market products and ideas better, drive revenues, and connect with consumers, customers and partners. Many companies have realized the opportunities that APIs offer and have launched their own platforms and applications to deliver products and services. The popularity of APIs isn’t limited to social media. APIs are strategic tools to unlock business value. Check out how extensive APIs are by reviewing the API directory found at ProgrammableWeb.
Just like conventional APIs are making it easy for programmers to create applications, similarly Predictive APIs are making machine learning simple and accessible to everyone. This type of APIs are making it easier to apply machine learning to data — and thus to create Predictive Apps. In essence, these APIs abstract away some of the complexities of creating and deploying machine learning models and make machine learning more accessible to developers. They also allow them to spend more time on user experience, design, data munging, experimenting and delivering value from data.
In simple terms, machine learning is a computer’s ability to learn from data, and it is one of the most useful tools we have to develop intelligent systems and applications. Machine learning is used widely today for all kinds of tasks, from churn prediction in large companies, to web search, to medical diagnostics, to robotics. It’s hard to find a field that cannot benefit from machine learning in one way or another. Predictive analytics and machine learning are bringing new levels of speed, relevance, and precision to the way we design and manage operating models. In health care, machine learning is changing the way doctors identify people at risk of developing certain diseases; in retail, machine learning is used to analyze purchasing data to anticipate trends; CRM and marketing experts use it to tailor campaigns and offers.
Machine learning is fun once you know what it is and how to use it. Predictive APIs provides great opportunity for all of us to use this super awesome capability with just enough Math to make awesome applications. Organizations can analyze data and predict future outcomes with Predictive APIs. They have the opportunity to use these APIs to build smart and intelligent apps using machine learning algorithms.
“Machine learning, predictive analytics and APIs for that matter are not technologies of the future, but important technologies of the present.” – Janet Wagner, Machine learning and predictive analytics foster growth
Read this post to understand the business possibilities enabled by Predictive APIs. To learn how to use Predictive APIs and make machine learning work for you I highly recommend reading Louis Dorard’s book “Bootstrapping Machine Learning”.
Who do you believe is the #1 at the Kaggle Rankings?
A) A professor from Stanford with 20 years experience in machine learning.
B) A Russian mathematician who solved college level math puzzles at the age of 3 and works for the KGB.
C) A Spaniard from Andalusia (where my hometown is and where everybody naps twice a day) who works for a hospital.
D) Chuck Norris
Of course answer was C. The point he was trying to make was that we don’t need machine learning gurus and academics instead we need experts and professionals from other fields who know enough about machine learning to use it.
Since then we have been collaborating on many initiatives related to promoting machine learning and making it more accessible and simple. Earlier this year, Francisco introduced me to Louis Dorard who is actively helping people to exploit the power of machine learning with minimal coding experience using Predictive APIs. In the last 6 months or so three of us discussed the need for a community and platform to bring together practitioners from industry, academia and public services to present new developments, identify new needs and trends, and discuss the challenges of building real-world predictive APIs and applications. Last month we announced that PAPIs 2014 – The First International Conference on Predictive Application and APIs will be held in Barcelona on November 17-18. A technical and practical conference dedicated to Predictive APIs and Predictive Apps
PAPIs ‘14 is the first International Conference on Predictive APIs and Apps. It will take place on 17-18 November 2014 (right before the O’Reilly Strata Conference) in Barcelona, Spain, where it will connect those who make Predictive APIs with those who use them to make Predictive Apps.
We want PAPIs to become an open forum for technologists, researchers and developers of real-world predictive APIs and applications to get together to learn and discuss new machine learning APIs, techniques, architectures, and tools to build predictive applications.
With the barrier of entry for machine learning effectively removed by predictive APIs, the time is now for all of us (programmers, researchers, business and technology professionals) to take advantage of machine learning to deliver real and meaningful economic and social value. Just remember, data alone is not enough. We need predictive APIs and applications to make it valuable, actionable and meaningful.
See you all at PAPIs in Barcelona!