In this contributed article, Jules S. Damji, an Apache Spark Community Evangelist with Databricks, shows how as the value of data continues to grow, the next-generation smart grid should become a reality, benefiting utility companies and consumers alike.
In this contributed article, Lisa Orr, senior data scientist at Urban Airship, describes how her team predicted mobile app user churn and Urban Airship trained and scaled their machine learning model over the last year — and how now it’s reaping valuable insights.
In this contributed article, Dan Adika, CEO and cofounder of WalkMe, discusses how big data, combined with machine learning and artificial intelligence, can contextually guide employees on how to use any system, provide businesses with insights into common technology obstacles, and ultimately personalize the user experience to drive greater adoption.
At The Data Incubator we pride ourselves on having the latest data science curriculum. Much of our curriculum is based on feedback from corporate and government partners about the technologies they are looking to learn. However, we wanted to develop a more data-driven approach to what we should be teaching in our data science corporate […]
In this contributed, Amir Noghani, SEO specialist and the general manager at Green Web Marketing, takes a look at Google’s RankBrain, its machine learning, artificial intelligence system, and how it is forcing curators of website content to do what they should have been doing all along, creating quality content for their websites.
Cloudera to Accelerate Data Science and Machine Learning for the Enterprise with New Data Science Workbench
Cloudera, the provider of a leading platform for machine learning and advanced analytics built on the latest open source technologies, today unveiled Cloudera Data Science Workbench, a new self-service tool for data science on Cloudera Enterprise which is currently in beta.
The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. This is the fourth in a series of articles providing content extracted from the guide. The topic for this segment is the results of the recent “insideHPC insideBIGDATA AI/Deep Learning Survey 2016” underwritten by NVIDIA.
I recently caught up with Paulo Sampaio, Data Scientist at EDITED, to talk about applying machine learning, neural networks, natural language processing, and big data analytics to the retail industry. Paulo and his team are applying neural networks, machine learning and other models to analyze over 520 million products in real-time across 42 countries to make gradual distinctions in clothing styles, sizes and categories.
In this contributed article, Smita Adhikary, Managing Consultant at Big Data Analytics Hires, provides a whirlwind overview of machine learning technology and why it’s important to increasing the value of enterprise data assets.
IBM announced IBM Machine Learning, the first cognitive platform for continuously creating, training and deploying a high volume of analytic models in the private cloud at the source of vast corporate data stores. Even using the most advanced techniques, data scientists – in shortest supply among today’s IT skills* – might spend days or weeks […]