Lessons from the field on managing data science projects and portfolios The ability to manage, scale, and accelerate an entire data science discipline increasingly separates successful organizations from those falling victim to hype and disillusionment. Data science managers have the most important and least understood job of the 21st century. This paper from Domino Data […]
Intel’s New Processors: A Machine-learning Perspective
October 31, 2017 by
Machine learning and its younger sibling deep learning are continuing their acceleration in terms of increasing the value of enterprise data assets across a variety of problem domains. A recent talk by Dr. Amitai Armon, Chief Data-Scientist of Intel’s Advanced Analytics department, at the O’reilly Artificial Intelligence conference, New-York, September 27 2016, focused on the usage of Intel’s new server processors for various machine learning tasks as well as considerations in choosing and matching processors for specific machine learning tasks.
MapR Delivers Self-Service Data Science for Leveraging Machine Learning and Artificial Intelligence
October 30, 2017 by
MapR Technologies, Inc., a pioneer in delivering one platform for all data, across every cloud, announced the MapR Data Science Refinery, a new solution that provides data scientists an easy way to access and analyze all data in-place, to collaborate, build and deploy machine learning models on the MapR Converged Data Platform.
Data 2020: State of Big Data Study
October 22, 2017 by
Our friends over at SAP recently published a study that highlights some really compelling findings around data scientists. There’s a wide gap and serious discrepancy in the level of importance organizations place on data scientists and the number of data scientists they employ. Below are some key statistics from the study, along with a summary infographic.
Book Review: Python Data Science Handbook
October 17, 2017 by
I recently had a need for a Python language resource to supplement a series of courses on Deep Learning I was evaluating that depended on this widely used language. As a long-time data science practitioner, my language of choice has been R, so I relished the opportunity to dig into Python to see first hand how the other side of the data science world did machine learning. The book I settled on was “Python Data Science Handbook: Essential Tools for Working with Data” by Jake VanderPlas.
Probing the Wisdom of Apple, Inc., Crowds Using Alternative Data Sources
October 12, 2017 by
In this contributed article, Anasse Bari, clinical assistant professor of computer science at New York University, and software engineer Lihao Liu, provide a detailed look at the competitive analysis they performed for four major smartphone contenders: iPhone X and 8, Samsung Galaxy Note 8, Nokia 8 and Google Pixel 2 using alternative data sources.
DialogTech Helps Businesses that Value Phone Calls Drive Growth with AI and Predictive Analytics
October 9, 2017 by
DialogTech, a leading provider of actionable marketing analytics for phone calls, announced the addition of a new team of data scientists to help businesses that value phone calls unlock the full power of artificial intelligence to drive growth.
IBM Unveils a New High-Powered Analytics System for Fast Access to Data Science
October 5, 2017 by
IBM (NYSE: IBM) announced the Integrated Analytics System, a new unified data system designed to give users fast, easy access to advanced data science capabilities and the ability to work with their data across private, public or hybrid cloud environments.
Domino Data Lab Accelerates Model Delivery on AWS
September 29, 2017 by
Today Domino Data Lab announced general availability of its Domino Model Delivery product. Built to run natively on Amazon Web Services (AWS), this offering makes the process of deploying highly scalable production models faster and more cost effective.
Anaconda Enterprise 5 Introduces Secure Collaboration to Amplify the Impact of Enterprise Data Scientists
September 10, 2017 by
Anaconda, the Python data science leader, introduced Anaconda Enterprise 5 software to help organizations respond to customers and stakeholders faster, deliver strategic insight for rapid decision-making and take advantage of cutting edge machine learning.
