Overview: Innominds recently hosted a dinner series where all the industry leaders from big data joined together to discuss the impact of big data; It’s shortcomings and to lay a future roadmap on how product companies should structure their big data solution.
Cloud migration is a substantial decision with no "one-size-fits-all" solution. Your business is very unique, and so should be your cloud migration plan. Your plan certainly needs to be tailored to suit the unique nature of your business so as not to fall into a trap of failure, as we see many companies doing once they start on migration. As we all are aware, cloud migration is not as easy as swapping the infrastructure and calling it a day. It must be personal, well-thought, well-engaged and focus on long-term gains.
Digital business is shifting from a future strategic vision by IT leaders and digital leaders to providing a true competitive edge. New digital technologies are transforming the way your enterprise business operates, as you begin the balance of delivering better service to your customer, improving efficiency, and cutting costs.
Leaders of the Big Data and analytics industry gathered last month in New York for the Strata + Hadoop World conference. Over 7,000 people attended the event where keynote speakers, including White House chief data scientist DJ Patil, laid out their visions for where machine learning, analytics, the Internet of Things, autonomous vehicles and smart cities will be taking us in the near future. This year’s event truly confirmed that streaming real-time analytics has moved into the mainstream of data science. It is quickly becoming a reality.
Long ago, back around 2007, we all were introduced to the 3rd Platform, with cloud as its core and solutions that offered anytime, anyplace access to application functionality. The platform was built on the technology pillars of mobile computing, cloud services, big data and analytics and social networking.
Innominds’ iFusion Analytics solves the data challenges that hinder enterprises today. Our data mining framework iFusion:
Many a times, data scientists and analysts input data and then train a model like logistic regression for classification. Most of the practitioners do not seem to spend enough time on this part of the output and instead focus only on the top level diagnostics consisting of coefficient summary, RMSE or classification matrix and may be the overall measure such as an R2 in the case of a linear regression or a C-Stat or an area under curve (AUC) for logistic regression. While not denying the value of these top level diagnostics, it is also important to check for model fit than just the estimation or classification error. This is when the deviance based diagnostics come handy.