•  Scalable machine learning for smarter Big Data predictions | #BigDataSV
  Saroj Kar | March 11th
  Machine learning helps enterprises use all of their data for better real-time predictions, improved decision-making processes and analyzing patterns. SriSatish Ambati, the CEO of H2O.ai (formerly 0xdata, Inc.) stopped by theCUBE during SiliconANGLE’s BigDataSV meet up with co-hosts John Furrier and Jeff Kelly to discuss how machine learning enables users to get more value from their existing data and easily create smarter business models.
  The machine learning product of H2O is an open source, parallel processing system that is developed for high performance and scalability. With this system H2O hopes to woo data scientists and businesses with a powerful yet easy to use data analysis platform.
  According to Ambati, machine learning is the new SQL. In the past, SQL defined data in the form of databases, but machine learning is driving better data-driven predictions. He mentioned scenarios such as fraud prevention, pattern recognition and faster predictive analytics as all part of the machine learning tool set.
  
  “People have built static internet, they have built data driven internet, and they now want to build smarter internet using the machine learning,” Ambati said. “Now we have a three way mix between data, Internet of Things and intelligence,” he continued.
  Big Data in business: Operations and beyond
  When asked about Big Data’s role in operational efficiency, Ambati said it’s now more important than ever. Systems are collecting lots of data and often these traditional dashboards are seen as the steering factors for the organization, as strategic decisions are made on the basis of the information provided.
  He then added deep learning is another big trend for businesses. H2O has worked on sophisticated machine learning algorithms and not just simple algorithms to run logistic regression, boosting machine that helps companies predict data with ease, speed and more accuracy.
  With the scoring engine that H2O developed, Ambati said apps are built that can now dynamically change based on incoming data. “The scoring engine is nano second fast and with this you can do hundreds of predictive models.”
  Commenting on how H2O is helping developers, Ambati said his company is working to add more algorithms. It is now offering H2O as a real-time machine learning service that can be used on a smartphone app or web app that learn continually as it receives more data. Thus giving developers access to smarter predictive insights for said data.
  
  
  There was a phrase in Dr Saxe talk that will stick with me.
  A little hardware at the front edge of the IoT. In the skin of the IoT.
  

  Note this article is about a wearable, but it was printed in an Artificial Intelligence magazine.
  Forget Kalmans as it would take a million engineers, and machine learning will do all of that for us.
  Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure
  Nisarg Vyas, Jonathan Farringdon, David Andre, John Ivo Stivoric
  Abstract
  In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss.

  We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

  
  Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
  This shows that there will be a drive to have the sensors “always on” in the skin of the IoT….just as in Dr. Saxe’s talk
  to give the data for the machine learning to give something back.
  The something back will be both for the individual. but also for the larger systems we are a part of.
  Without the sensors there is no machine learning and nothing comes back to the induvidual or the larger
  cities/culture.
  
  
  
  t

  Many of the most disruptive insights from massive log data will be of this nature: complex, buried, and unprecedented. Learning from the log data itself, rather than from any a priori knowledge, will be how many data scientists spend much of their time. They will increasingly tune their machine-learning algorithms to listen for “signals” in the log that even the most advanced human subject-matter experts had previously overlooked.
  
  
  

  For the casual reader, who will say, “What the heck does this have to do with QUIK?”
  This thread is born out of the Dr Saxe talk on IoT,
  Which can be watched from the link on the QUIK site.
  The importance of machine learning to take the data from the sensors on the edge, where QUIK will be,
  and tell you something back…the basis of Dr Saxe talk.
  We used to have to put the data in the system, and now we won’t have to do that anymore.
  It’s a very thought provoking talk on many levels.
  What did I learn from the talk, besides new reading material?
  QUIK’s roadmap is not a narrow footbridge, it is broad, far reaching, beyond what I had considered until that talk.
  Do I think they are doing some things, or have something for this IoT?
  You bet, but I don’t know where it is on the roadmap….the S4?
  Why? The others fall away, QUIK can own this space, as software can’t fit in here!
  

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