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News, reviews and feature articles on companies that are changing how organizations leverage massive volumes of data

Movies, Neural Networks Boost AI Language Skills

When we discuss about artificial intelligence (AI), how are machines learning? What kinds of projects feed into greater understanding? For our friends over at IBM, one surprising answer is movies. To build smarter AI systems, IBM researchers are using movie plots and neural networks to explore new ways of enhancing the language understanding capabilities of AI models.

IoT Feeds Data Stream to AI Application Converge for Real-time, Sideline Insights

In this contributed article, Jason Mann, Vice President of Internet of Things (IoT) at SAS, discusses how IoT creates enormous streaming data volumes, much of it unused or lost. Data lost is opportunity missed. The key to unlocking the value of IoT data is with AI applications that self-learn and automate actions. AI applications – created with analytics and using real-time IoT data – can reveal new business opportunities long before the competition wakes up.

Interview: Jamie Engesser, VP Product Mangement at Hortonworks

I recently caught up with Jamie Engesser, VP Product Mangement at Hortonworks during the company’s DataWorks Summit 2018 conference in San Jose, California, to get an update on his company’s direction and his sense for the pulse of the big data industry.

Databricks Partners with RStudio To Increase Productivity of Data Science Teams

Databricks, a leader in unified analytics and founded by the original creators of Apache Spark™, announced a partnership with RStudio, providers of a free and open-source integrated development environment for R, to increase the productivity of data science teams. The partnership will allow the two companies to seamlessly integrate Databricks’ Unified Analytics Platform with the RStudio Server, simplifying R programming on big data.

Field Report: DataWorks Summit 2018

In this field report I wanted to give you a sense for what the vendor ecosystem was saying at DataWorks Summit, their corporate message if you will. Each company had a somewhat different slant of course which aligned with their products and services, but there was also a lot of commonality. Most everyone had some tie into the industry’s current buzz – AI, machine learning and deep learning. This was perfect for me as a practicing data scientist myself. Let’s get started with some vendor snapshots …

Advancements in Dynamic and Efficient Deep Learning Systems

We’re seeing much hype in the marketplace about the potential of AI, especially with respect to computer vision systems and its ability accelerate the development of everything from self-driving cars to autonomous robots. To create more dynamic and efficient deep learning systems, that don’t compromise accuracy, IBM Research is exploring new and novel computer vision techniques from both a hardware and software angle.

Hortonworks Data Platform 3.0 Enables Containerization and Deep Learning Workloads

Hortonworks, Inc.® (NASDAQ: HDP), a leading provider of global data management solutions, today announced Hortonworks Data Platform (HDP) 3.0, which delivers significant new enterprise features including containerization for faster and easier deployment of applications, and increased developer productivity. The new version of HDP enables customers to more quickly, reliably and securely get value from their data at scale to drive business transformation.

Living On the Edge: Extracting Ultimate Value from Your IoT Data

In this contributed article, Jerry Baulier, Vice President of IOT R&D at SAS describes IoT and streaming analytics and the potential lifesaving benefits from that real-time data. Streaming data allows you to assemble meaning from IoT data when you need it, both in real time and historically to identify trends in cross-sensor analysis. By processing data on the edge, organizations, individuals and communities are benefiting from the insights offered by real-time data.

Databricks Conquers AI Dilemma with Unified Analytics

Databricks, a leader in unified analytics and founded by the original creators of Apache Spark™, addresses this AI dilemma with the Unified Analytics Platform. The company launched new capabilities to lower the barrier for enterprises to innovate with AI. These new capabilities unify data and AI teams and technologies: MLflow for developing an end-to-end machine learning workflow, Databricks Runtime for ML to simplify distributed machine learning; and Databricks Delta for data reliability and performance at scale.

Apache Spark 2.0: A Deep Dive Into Structured Streaming

In this talk, Tathagata Das takes a deep dive into the concepts and the API and show how this simplifies building complex “Continuous Applications”. Tathagata is an Apache Spark Committer and a member of the PMC. He’s the lead developer behind Spark Streaming, and is currently employed at Databricks.