In sync with the upcoming release of Splunk’s Machine Learning Toolkit 5.2, we have launched a new release of the Deep Learning Toolkit for Splunk (DLTK) along with a brand new “golden” container image. This includes a few new and exciting algorithm examples which I will cover in part 2 of this blog post series.
In part 1 of this release blog series we introduced the latest version of the Deep Learning Toolkit 3.1 which enables you to connect to Kubernetes and OpenShift. On top of that a brand new “golden image” is available on docker hub to support even more interesting algorithms from the world of machine learning and deep learning! Over the past few months, our customers’ data scientists have asked for various new algorithms and use cases they wanted to tackle with DLTK.
For those of you with some gray hair working in the manufacturing business, remember when order intake plunged suddenly by more than 40%? Remember when CFO and Controllers ruled the company, driving painful cost-cutting programs to counter double-digit business losses? It was the time of the Economic and Financial Crisis 2007/08, which forced manufacturing organizations to stare in the abyss.
Splunk’s recent "What Is Your Data Really Worth?" report1 highlighted the importance of data and analytics to financial services companies. In our global survey of business and IT decision makers2, 89% of respondents from financial services companies felt that the intelligent use of data and analytics is becoming the only source of differentiation in the industry.
I recently participated in a webinar exploring the question "What is Your Data Really Worth?" in the context of financial services. Enterprise Strategy Group (ESG), in partnership with Splunk, performed a global research survey of 1,350 business and IT decision-makers across leading economies and industries. Over the course of the webinar we discussed their findings with my participation focused on IT Operations.
