1. Machine learning
With
Sabyasachi Upadhya

2. When Amazon recommends a book you would like or Google
predicts your schedule and Pandora magically creates a
playlist suited to your likes, it is machine learning on Big
Data.
With Big Data projected to drive enterprise IT spending to
$100 billion according to Gartner, Big Data is here to stay,
and as a result, more businesses of every size are getting
into the game. For enterprise organizations Big Data is a
strategic asset. Each customer, partner, or supplier response
or non-response, transaction, defection, credit default, and
complaint provides the enterprise the experience from
which to learn. From a consumer perspective, every action
performed online, every sales process, product interaction,
prescribed drug, and environmental anomaly, is being
tracked by various sources.

3. Only with advanced analytics, and specifically machine
learning, can companies truly tap into their rich vein of
experience and mine it to automatically discover insights and
generate predictive models to take advantage of all the data
they are capturing. This advanced analytics technology means
that instead of looking into the past for generating reports,
businesses can predict what will happen in the future based
on analysis of their existing data. The value of machine
learning is rooted in its ability to create accurate models to
guide future actions and to discover patterns that we’ve
never seen before.

4. Defining Machine Learning
 Machine learning is the modern science of finding
patterns and making predictions from data based
on work in multivariate statistics, data mining,
pattern recognition, and advanced/predictive
analytics.

6. Machine learning methods are particularly effective in
situations where deep and predictive insights need to be
uncovered from data sets that are large, diverse and fast
changing — Big Data. Across these types of data, machine
learning easily outperforms traditional methods on
accuracy, scale, and speed. For example, when detecting
fraud in the millisecond it takes to swipe a credit card,
machine learning rules not only on information associated
with the transaction, such as value and location, but also
by leveraging historical and social network data for
accurate evaluation of potential fraud.

7. Machine learning methods are vastly superior in analyzing
potential customer churn across data from multiple
sources such as transactional, social media, and CRM
sources. High performance machine learning can analyze
all of a Big Data set rather than a sample of it. This
scalability not only allows predictive solutions based on
sophisticated algorithms to be more accurate, it also
drives the importance of software’s speed to interpret the
billions of rows and columns in real-time and to analyze
live streaming data.

8. For those of us who are practicing and developing machine
learning technology, it’s no longer sufficient to provide the
ability to achieve the most accurate, fast, and scalable
predictive insights. Ultimately, for machine learning to
impact the world around us in a truly meaningful way, we
have to deliver Machine Learning in a smarter, more
usable form. By enabling not only the data scientists who
have PhDs but also the business users to tap into the
state-of-the-art machine learning technology, we will truly
bring this technology to the masses and dramatically
accelerate time-to-insight for organizations of all sizes.