Hewlett Packard Enterprise acquired the business assets of MapR Technologies, Inc., in data platform for AI and analytics applications powered by scale-out, multi-cloud and multi-protocol file system technology. This transaction includes MapR's technology, IP, and domain expertise in AI and ML and analytics data management. HPE welcomes MapR customers and partners and plans to support existing deployments along with ongoing renewals. "The explosion of data is creating a new era of intelligence where the winners will be the ones who harness the power of data, wherever it lives," said Antonio Neri, president and CEO, HPE. "MapR's file system technology enables HPE to offer a complete portfolio of products to drive AI and analytics applications and strengthens our ability to help customers manage their data assets end to end, from edge to cloud."

Many enterprises have some form of artificial intelligence and machine learning in place, whether it's in the form of a pilot program, a proof-of-concept in the cloud, or even a production implementation. Even though machine learning has been around for a long time, it's still an emerging technology for many enterprises. For those who have created a pilot or proof-of-concept, you may have cleared the first hurdle in implementing these technologies. But putting them into production, even on a limited basis, may present challenges that you have not yet considered. Putting large-scale systems in place that deliver value is an entirely new proposition.

IT media platform CIOReview recently named the 20 Most Promising Big Data Solution Providers – 2018. The listing features providers that are assisting the enterprise with machine learning, artificial intelligence, data governance, cloud computing and real-time analytics. AI in particular is generating much of the interest, and 2017 showed us that big data could present even deeper use cases such as fraud detection and pattern recognition, pushing the market well passed traditional algorithms. At Solutions Review, we track the solution providers that have the biggest impact on the enterprise. As such, we've read through the awards, available here, and selected the solution providers that are most interesting to us.

IT media platform CIOReview recently named the 20 Most Promising Big Data Solution Providers – 2018. The listing features providers that are assisting the enterprise with machine learning, artificial intelligence, data governance, cloud computing and real-time analytics. AI in particular is generating much of the interest, and 2017 showed us that big data could present even deeper use cases such as fraud detection and pattern recognition, pushing the market well passed traditional algorithms. At Solutions Review, we track the solution providers that have the biggest impact on the enterprise. As such, we've read through the awards, available here, and selected the solution providers that are most interesting to us.

Ever since I first heard about Tensorflow and the promises of Deep Learning I've been anxious to give it a whirl. Tensorflow is a powerful and easy to use library for machine learning. It was open-sourced by Google in November 2015. In less than 2 years it has become one of the most popular projects on GitHub. I was introduced to Tensorflow at the O'Reilly Strata Data conference in San Jose last year. During a presentation by developer evangelists from Google I saw several really fun image processing examples that used Tensorflow to transform or identify subjects in images.

MapR is introducing six new data science offerings aimed at customers that are at varying points in their data science journey at the Strata Data Conference happening in NYC today. The six offers are the AI/ML Hack-a-thon, MapR Data Science Refinery, Cybersecurity Advanced Protection, ML Deployment, AI Enablement and ML Rendezvous Orchestration. According to the company, because AI and ML are complex, organizations cannot always execute AI and ML ideas, and those that do may not be able to easily bring it to production. The six new offerings are designed to help customers no matter where they are in their AI and ML journey, whether they are just starting or have already invested resources. In addition to increasing in complexity throughout the list, the offerings take more time the more complex they are, with the first one taking one week to prepare and one day to deliver and the final offering taking up to eight weeks.

Sometimes when we write about analytics, machine learning and AI, it's challenging to come up with concrete use cases. That makes it harder than it should be for readers to grasp the power of these technologies. And that's a shame, because it makes AI seem ethereal rather than useful or easily understood. But every so often I am reminded that when one needs use cases, one need look no further than ERP (Enterprise Resource Planning) software. Sometimes ERP is disparaged as mundane.

Book will be released 26 Sept 2017: Visit MapR booth for free book signings or to talk about logistics 44.

"We're excited to partner with MapR to bring our enterprise data science platform to their customers," said William Merchan, Chief Strategy Officer, DataScience.com. "By integrating the MapR Persistent Application Container Client, data science jobs are deployed directly on the MapR cluster for fast data access and better resource utilization. This is a huge differentiator for us compared with other data science workbenches on the market. Partnering with the leaders in the enterprise data platform space greatly enhances the ability of our customers to scale their analyses and deploy models to mission-critical production applications." Data scientists can now collaborate to build and deploy models via a full-featured platform from DataScience.com and MapR that provides convenient access to a wide range of data science tools and notebooks integrated directly with MapR.

This week MapR announced a new solution called Quick Start Solution (QSS), focusing on deep learning applications. MapR touts QSS as a distributed deep learning (DL) product and services offering that enables the training of complex deep learning algorithms at scale. Here's the idea: deep learning requires lots of data, and it is complex. If MapR's Converged Data Platform is your data backbone, then QSS gives you what you need to use your data for DL applications. It makes sense, and it is in line with MapR's strategy.