How to use HCP to filter, analyze, and investigate alerts.

A smart factory can gather a significant amount of data generated by thousands of sensors embedded in equipment in the manufacturing process. The enabled smart factory optimizes maintenance, anticipates failures and helps make the manufacturing process more efficient.

Cloudera Fast Forward Labs’ latest applied machine learning research report is about boosting natural language processing (NLP) with transfer learning. Organizations large and small have volumes of valuable data stored as free-form text yet the scale of data combined with the complexities of language processing makes using it to drive insight and automation a challenge.

Cloudera Fast Forward Labs’ latest applied machine learning research report focuses on advancements in Deep Learning for Image Analysis. Research and commercial interest in deep learning has exploded in the last five years, driving remarkable advancements across applications including medical imaging, autonomous vehicles, news and media (including manipulation), and art.

Find out how one Telco delivered their big data goals over 4 years.

This video explains a step by step process for getting an HDP 3 cluster up and running on Centos 7 using 2 nodes: Services installed on Ambari node: ambari-server, Services installed on node1: SmartSense, Ambari Metrics.

Using Cloudera technology as the platform, a connected car can gather a significant amount of data, ranging from other vehicles, road infrastructure and even pedestrians. The connected car ecosystem enriches the driving experience and especially makes for a safer driving environment.

At Cloudera, we believe data can make the impossible possible. The world’s leading organizations choose Cloudera to transform their businesses, improve lives, and advance human achievement. All through the power of data.

Cloudera Fast Forward Labs’ latest machine learning research report introduces a new machine learning capability that relaxes the stringent labeled data requirement in supervised machine learning thereby opening up new product possibilities.

The key challenges preventing IoT initiatives to be successful are the inability to capture and process data directly from thousands of edge devices as well as the lack of operational visibility and control of the edge. Dinesh Chandrasekhar, Director of Product Marketing for Data-in-Motion at Cloudera, talks about solutions designed to solve these IoT challenges.