Cloudera Gives Data Scientists More Options for ML

Cloudera Gives Data Scientists More Options for ML

Cloudera unleashed a collection of new software today that’s geared at accelerating the development and deployment of machine learning programs. Read more…

The Seven Sins of Data Prep

The Seven Sins of Data Prep

Data preparation is often considered a necessary precursor to the “real” work found in visualizing or analyzing data, but this framing sells data prep short. Read more…

Why Developers Need to Think like Data Scientists

Why Developers Need to Think like Data Scientists

Data is growing faster than is even fathomable. By 2020, roughly 1.7 megabytes of new information will be created every second for every human being on the planet. Read more…

Danger and Difficulty Temper Data’s Huge Potential

Danger and Difficulty Temper Data’s Huge Potential

It has been called the new oil, the new currency, the new religion. It is data, of course, and it’s having a monumental impact on how we build business systems in the 21st century. Read more…

Datanami Headlines

Google Unleashes TPUs on Cloud ML Engine

As the amount of machine learning training data soars, so too does demand for new tools that will accelerate the process. With that in mind, Google Cloud announced the beta release of a new feature that allows users to speed training by running Tensor processing units (TPU) on its machine learning engine. Read more…

Oracle Deal Adds Data Tools to its Cloud Services

Oracle moved this week to expand its portfolio of data tools and libraries with the acquisition of DataScience.com, developers of a collaboration platform that connects with popular workflows and cloud infrastructure.

Oracle said Wednesday (May 16) the addition of the Los Angeles-area data science startup would help boost utilization of machine learning on Oracle Cloud. Read more…

Big Data File Formats Demystified

So you’re filling your Hadoop cluster with reams of raw data, and your data analysts and scientists are champing at the bit to get started. Then the question hits you: How are you going to store all this data so they can actually use it? Read more…

MemSQL Gains Traction, New Investors

MemSQL, the database vendor that touts its architecture as capable of handling both data analytics and transactions, announced a $30 million funding round led by, GV, the former Google Ventures.

The hybrid SQL database developer said this week its latest funding round brings its total to $110 million. Read more…

A Wave of Purpose-Built AI Hardware Is Building

Google last week unveiled the third version of its Tensor Processing Unit (TPU), which is designed to accelerate deep learning workloads developed in its TensorFlow environment. But that’s just the start of a groundswell of new processors and processing architectures, including Wave Computing, which claims its soon-to-be-launched processor will dramatically lower the barrier of entry for running artificial intelligence workloads. Read more…

Hive Rolls Productivity Platform

Among the latest pitches for adopting predictive analytics are worker productivity tools that help enterprises understand what’s around the corner. The ability to forecast tomorrow’s workload is touted as a way to improve efficiency and even reduce workplace stress by eliminating “reactive work.”

That’s the value proposition being offered by Hive, the productivity startup that unveiled an analytics feature this week combining task management and request messaging. Read more…

How Disney Built a Pipeline for Streaming Analytics

The explosion of on-demand video content is having a huge impact on how we watch television. You can now binge watch an entire season’s worth of Grey’s Anatomy at one sitting, if that suits your fancy. Read more…

Inside One VC Firm’s Hands-On Approach to AI

There’s no shortage of tech entrepreneurs taking venture capital money to turn their artificial intelligence ideas into AI reality. These startups typically fill up at the VC spigot and go back to their offices to build product, with check-ins from the VC firm along the way. Read more…

AWS Adds Do-Over Feature to Aurora Database

Amazon Web Services, which added new capabilities to its Aurora scale-out relational database last year, said this week it is including an “Undo” option with the production platform.

The Amazon Aurora Backtrack feature “is as close as we can come, given present-day technology, to an Undo option for reality,” Jeff Barr, chief evangelist at AWS, explained in announcing the upgrade in a blog posted Thursday (May 10). Read more…

IBM Bolsters Storage with AI, De-Dupe, and Cloud DR

Organizations today are struggling to keep up with the massive amount of data they’re generating and collecting, and in some cases they’re close to maxing out their existing storage. In a way it’s good news for storage providers, but IBM says it wants to help customers by taking a smart approach to minimizing their data and maximizing their storage investment. Read more…

Emerging Advanced Scale Tech Trends Focus of Annual Tabor Conference

At Tabor Communications’ annual Advanced Scale Forum (ASF) held this week in Austin, the focus was on enterprise adoption of HPC-class technologies and high performance data analytics (HPDA). It’s a confab that brings together end users (CIOs, IT planners, department heads) and vendors and encourages group discussion and debate to go along with presentations and panel discussions. Read more…

‘Lifelong’ Neural Net Aims to Slash Training Time

Among the consequences of big data is a wealth of relevant minutiae that can be used to train machine learning and other models. That often translates into processing-intensive steps required to train models to perform a specific task. Read more…

The 10 Step Guide to Mastering Machine Learning

Artificial intelligence (AI) and machine learning are transforming the global economy, and companies that are quick to adopt these technologies will take $1.2 trillion from those who don’t. Businesses that fail to take advantage of predictive analytics, or don’t have the time or resources – like highly-trained (and expensive) data scientists – will fall behind organizations that embrace AI and machine learning to extract business value from their data. Read more…