How Active Learning Improves Nighttime Pedestrian Detection
Active learning makes it possible for AI to automatically choose the right training data. An ensemble of dedicated DNNs goes through a pool of image frames, flagging frames that it finds to be confusing. These frames are then labeled and added to the training dataset. This process can improve DNN perception in difficult conditions, such as nighttime pedestrian detection.
Hear LinkedIn’s senior SRE, Todd Palino, share how the company continually improves the state of its infrastructure, so that the developers who are rolling out applications have a framework that they can do it within, and they can do it safely. LinkedIn currently generates over 50 terabytes a day of unique metrics on applications. No human is going to look at 50 terabytes a day of data and get anything useful out of it, so LinkedIn relies on systems give them some useful signal out of all that noise. By moving down the road of machine learning, LinkedIn can now do anomaly detection using machine learning models.
CA Technologies’ Chris Kline shares how to adopt an AIOps strategy in a DevOps world. Chris shares how AIOps enables a move away from siloed operations management and provides intelligent insights that drives automation and collaboration for continuous improvement. Since AIOps leverages big data, data analytics and machine learning to provide insight and enable a higher level of automation, no longer does IT Ops need to depend extensively on human operators for the management tasks that modern infrastructure and software require.
William Hill shares how AI and machine learning play a massive role in self-healing and the transformation that is taking place. Five years ago, companies would not be willing to even trust machines to start to make that decisional data. Today, it’s absolutely key to get visibility of all the piece of the jigsaw before making decisions on what can affect critical business services. AI helps analyze that metric explosion and make sense of it, because humans are limited by that. Not just understanding what the root cause of the problem is, but actually processing all this data coming through. https://www.williamhill.com/
Learn how AIOps will help US Bank increase automation across the tool chain by analyzing large, monitoring-driven data sets. US Bank shares how they have so much data to get through that the root cause correlation is a large part of the bank’s triage process today. With the help of automation, US Bank envisions that the resolution side will be more effective, delivering better up time and improving customer experience.
To demystify the complexity of Intelligent Automation, Capgemini developed the Five Senses of Intelligent Automation – their framework to help clients decode Intelligent Automation and translate it into real business value.