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IBM Makes Watson Available via API | #machinelearning #AI

IBM Makes Watson Available via API | #machinelearning #AI | e-Xploration | Scoop.it
IBM has upped the ante in the API game by making its Watson question-answering system available as a service. That’s right, Watson could soon power your smartphone app.

Via Ed Stenson
luiy's insight:

IBM didn’t have to flaunt its debatable cloud dominance over Amazon Web Services on the sides of public buses if it wanted to upstage the cloud kingpin at its user conference this week — Big Blue could have just led with the news that its famous, Jeopardy!-champ-destroying Watson system is now available as a cloud service.


That’s right: Developers who want to incorporate Watson’s ability to understand natural language and provide answers need only have their applications make a REST API call to IBM’s new Watson Developers Cloud. “It doesn’t require that you understand anything about machine learning other than the need to provide training data,” Rob High, IBM’s CTO for Watson, said in a recent interview about the new platform.

Fàtima Galan's curator insight, November 15, 2013 8:20 AM

"In order to encourage programmers to take advantage of the platform, IBM is working with venture capital firms — including New Enterprise Associates — to support and fund startups using the Watson API."

Scooped by luiy

Big Data Implementation Best Practices | The Big Data Hub

Big Data Implementation Best Practices | The Big Data Hub | e-Xploration | Scoop.it
Top 10 best practices that implementation teams should follow to increase the chances of success with big data projects (10 Big Data Implementation Best Practices http://t.co/xd32urVMra #analytics...
luiy's insight:

1. Gather business requirements before gathering data. Begin big data implementations by first gathering, analyzing and understanding the business requirements; this is the first and most essential step in the big data analytics process. If you take away nothing else, remember this: Align big data projects with specific business goals.

2. “Implementing big data is a business decision not IT.” This is a wonderful quote that wraps up one of the most important best practices for implementing big data. Analytics solutions are most successful when approached from a business perspective and not from the IT/Engineering end. IT needs to get away from the model of “Build it and they will come” to “Solutions that fit defined business needs.”

3. Use Agile and Iterative Approach to Implementation. Typically, big data projects start with a specific use-case and data set. Over the course of implementations, we have observed that organization needs evolve as they understand the data – once they touch and feel and start harnessing its potential value. Use agile and iterative implementation techniques that deliver quick solutions based on current needs instead of a big bang application development. When it comes to the practicalities of big data analytics, the best practice is to start small by identifying specific, high-value opportunities, while not losing site of the big picture. We achieve these objectives with our big data framework: Think Big, Act Small.

4. Evaluate data requirements. Whether a business is ready for big data analytics or not, carrying out a full evaluation of data coming into a business and how it can best be used to the business’s advantage is advised. This process usually requires input from your business stakeholders. Together we analyze what data needs to be retained, managed and made accessible, and what data can be discarded.

5. Ease skills shortage with standards and governance. Since big data has so much potential, there’s a growing shortage of professionals who can manage and mine information. Short of offering huge signing bonuses, the best way to overcome potential skills issues is standardizing big data efforts within an IT governance program.

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