Machine Learning in Big Data – Look Forward or Be Left Behind
Bill Porto, Senior Engineering Analyst, RedPoint Global Inc.
Computers? Not so much. One of the biggest developments – and challenges – in technology has been the advent of machine learning. But even as we make major strides in the age of Big Data, applying machine learning to our data is something that few have effectively achieved. Creating models to predict customer response or to segment customer data into set categories are “predictable” use cases. It’s a start – taking data, discovering what it can tell you, and creating a model and use for it. But that’s not enough…
If we want to deliver a real cost advantage for the enterprise with machine learning, there are larger, mission-critical issues to address. These issues focus on model choice, viability horizon, practical design alternatives, learning from on-the-fence model factors, and opportunities for automating access to changing data and netting-out error and noise.
This April at the Hadoop Summit in Dublin, I am delighted to have the opportunity to share with you how continual, adaptive optimization is the key to maintaining a leadership position in satisfying customer demand. As senior analytics engineer at RedPoint Global, I’m focused on developing automated business optimization software that incorporates evolutionary optimization, neural networks, and a host of other non-traditional machine learning techniques.
Hadoop Summit Europe 2016 provides a great opportunity to share my experience in machine learning and Big Data with attendees who want to really move ahead with their own machine learning mastery.
I’m looking forward to showing how to apply predictive modelling and optimization to harness the full power and potential of your data at the Hadoop Summit Europe 2016.
Register for the Hadoop Summit in Dublin – here: http://hadoopsummit.org/dublin/register/