In May we released the Splunk Machine Learning Toolkit (MLTK) version 5.2. We’ve loved telling you about some of the great new features, including the most recent blog on DensityFunction. However, we know that before you can start experimenting with model-building algorithms such as DensityFunction, your data needs to be prepared for machine learning. Machine learning operates best when you provide clean data as the foundation for building your models.

The 7.6 release of the Elastic Stack delivered the last piece required for an end-to-end machine learning pipeline. Previously, machine learning focused on unsupervised techniques with anomaly detection. However, several features have been released over the 7.x releases. In 7.2 Elasticsearch released transforms for turning raw indices into a feature index. Then 7.3, 7.4, and 7.5 released outlier detection, regression, and classification, respectively.

Use of AI can be critical when developing systems to support social good, with some inspiring examples using Splunk in healthcare and higher education organisations. According to our State of Dark Data report, however, only 15% of organisations admit they are utilising AI solutions today due to lack of skills. So how can we help organisations unlock the potential of AI?

Artificial intelligence refers to the machine's ability to learn and think. Given that it sort of mimics how humans think and reason, AI's application is virtually endless. AI reduces human error, do a task that is risky for humans to do, help humans solve complex, and so much more. With the emergence of artificial intelligence, concerns about data privacy have been brought into the light. Artificial intelligence relies on our personal information to learn.

Machine learning is the power cable for your business. Without it, your data center is a museum of hard drives. While machine learning can supercharge data-driven businesses, it requires both expertise and a complex suite of technologies to make it work. D2iQ’s KUDO for Kubeflow, which is in technical preview, is the enterprise platform designed to take you from prototype to production in no time.

A growing number of car companies have made their autonomous vehicle (AV) datasets public in recent years. Daimler fueled the trend by making its Cityscapes dataset freely available in 2016. Baidu and Aptiv respectively shared the ApolloScapes and nuScenes datasets in 2018. Lyft, Waymo and Argo followed suit in 2019. And more recently, automotive juggernauts Ford and Audi released datasets from their AV research programs to the public.

How annoying is it when you get a telemarketing call from a random phone number? Even if you block it, it won’t make a difference because the next one will be from a brand new number. Cyber attackers employ the same dirty tricks. Using domain generated algorithms (DGAs), malware creators change the source of their command and control infrastructure, evading detection and frustrating security analysts trying to block their activity.

Most IT and business leaders know that despite the economic and human disruption of the COVID-19 pandemic, digital transformation will ultimately speed up, not slow down. The immediate challenges of the pandemic have led companies to find innovative ways to get things done, relying on data-driven decisions and technologies.