Stephen Brobst is the Chief Technology Officer for Teradata Corporation. Stephen performed his graduate work in Computer Science at the Massachusetts Institute of Technology where his Masters and PhD research focused on high-performance parallel processing. He also completed an MBA with joint course and thesis work at the Harvard Business School and the MIT Sloan School of Management.
Stephen is a TDWI Fellow and has been on the faculty of The Data Warehousing Institute since 1996. During Barack Obama’s first term he was also appointed to the Presidential Council of Advisors on Science and Technology (PCAST) in the working group on Networking and Information Technology Research and Development (NITRD) where he worked on development of the Big Data strategy for the US government. In 2014 he was ranked by ExecRank as the #4 CTO in the United States (behind the CTOs from Amazon.com, Tesla Motors, and Intel) out of a pool of 10,000+ CTOs.
Analytics. It’s a $200 billion industry fueled by the speed, scale and competition of the rising digital economy. But with all the investments in analytics being made by businesses looking to gain an edge in this world, there’s one question no one seems to be asking: When do we stop buying into partial solutions that over-promise and under-deliver? The answer is “now.”
Teradata leverages all of the data, all of the time, so you can analyze anything, deploy anywhere, and deliver analytics that matter most to your business. We call it pervasive data intelligence, and only Teradata has the industry-leading products, expertise and services to deliver it today. It all starts with Vantage, the cloud-based platform for pervasive data intelligence. Vantage is the answer to the complexity, costs and inadequacy caused by today’s approach to analytics.
Teradata Vantage delivers artificial intelligence and machine learning with scale, to deliver timely, accurate answers and intelligent automation across your enterprise. With Teradata Consulting, you’ll have access to 5,000 experts around the globe and the ongoing services you need to implement pervasive data intelligence. So, you can remain focused on driving your digital transformation.
The continual investment, experimentation and uncertainty ends here. It’s time to stop buying “analytics.” And start investing in answers. Get the answer at teradata.com.
Eliminating Bias in the Deployment of Machine Learning
The primary source of bias in machine learning is not in the algorithms deployed, but rather the data used as input to build the predictive models. In this talk we will discuss why this is a huge problem and what to do about it. Different sources of bias will be identified along with possible solutions for remedying the situation when deploying machine learning. We will also speak about the importance of transparency when using machine learning to predict outcomes that impact critical decisions.
• Learn why most predictive models are biased.
• Learn about the sources of bias in predictive models.
• Learn how to reduce the negative impact of potential bias in predictive models.