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Big Data: Enterprise Class Machine Learning with Spark and MLbase
New Infrastructure Aims at Economical, In-memory, Large Scale Machine Learning

Machine Learning is a critical part of extracting value from Big Data. Choosing proper model, preparing data and getting usable results on large scale data is non-trivial exercise. Typically process consists of model prototyping using higher level, (mostly) single machine based tool like R, Matlab, Weka, then coding in Java or some other language for large scale deployment. This process is fairly involved, error prone, slow and inefficient.

Existing tools aiming at automating and improving this process are still somewhat immature and wide scale Machine Learning enterprise adoption is still low. Efforts are under way to address this gap i.e. to make enterprise class Machine Learning more accessible and easier.

Spark is new, purpose-built, distributed, in-memory engine that makes it possible to perform compute intensive jobs on commodity hardware clusters. One of applications Spark is targeted and especially suitable for is Machine Learning, key part in getting actionable insights from Big Data.

Machine Learning is compute intensive application, characterized by many iterative passes through data until optimal solution is found, and Spark is natural fit for such workloads.

MLbase (open source project) is ML platform  implemented on top of Spark which aims at easier and more productive implementation of ML algorithms.

Arguably most interesting part of MLbase will be ML Optimizer ( not released yet ), which will automate the task of choosing models.

Choosing proper model is difficult task and there are quite a few attempts to automate this process (one of the most interesting available products is Google Prediction API, a Cloud service which automatically evaluates, picks and executes model on submitted data).

About Ranko Mosic
Ranko Mosic, BScEng, is specializing in Big Data/Data Architecture consulting services ( database/data architecture, machine learning ). His clients are in finance, retail, telecommunications industries. Ranko is welcoming inquiries about his availability for consulting engagements and can be reached at 408-757-0053 or [email protected]

Silicon Valley Faculty Highlight
The platform combines the strengths of Singtel's extensive, intelligent network capabilities with Microsoft's cloud expertise to create a unique solution that sets new standards for IoT applications," said Mr Diomedes Kastanis, Head of IoT at Singtel. "Our solution provides speed, transparency and flexibility, paving the way for a more pervasive use of IoT to accelerate enterprises' digitalisation efforts. AI-powered intelligent connectivity over Microsoft Azure will be the fastest connected path for IoT innovators to scale globally, and the smartest path to cross-device synergy in an instrumented, connected world.
There are many examples of disruption in consumer space – Uber disrupting the cab industry, Airbnb disrupting the hospitality industry and so on; but have you wondered who is disrupting support and operations? AISERA helps make businesses and customers successful by offering consumer-like user experience for support and operations. We have built the world’s first AI-driven IT / HR / Cloud / Customer Support and Operations solution.
CloudEXPO.TV
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Testimonials
This week I had the pleasure of delivering the opening keynote at Cloud Expo New York. It was amazing to be back in the great city of New York with thousands of cloud enthusiasts eager to learn about the next step on their journey to embracing a cloud-first worldl."
@SteveMar_Msft
 
How does Cloud Expo do it every year? Another INCREDIBLE show - our heads are spinning - so fun and informative."
@SOASoftwareInc
 
Thank you @ThingsExpo for such a great event. All of the people we met over the past three days makes us confident IoT has a bright future."
@Cnnct2me
 
One of the best conferences we have attended in a while. Great job, Cloud Expo team! Keep it going."

@Flexential


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