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.
I recently chatted with Adam DeMattia from leading research and analyst firm ESG in a webinar about data use maturity in financial services. According to the research1, 21% of financial services firms identify as data innovators (compared to 11% of global respondents) — those who make smarter use of data as a matter of strategic importance.
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.
