At GrafanaCONline in June, we talked about the future of machine learning at Grafana Labs. Four months later, we are excited to introduce Grafana Machine Learning for Grafana Cloud, with our metrics forecasting capability. It’s available now to all customers on Pro or Advanced plans. If you’re not already using Grafana Cloud, you can sign up for a free 14-day trial of Grafana Cloud Pro here.
If you've ever wanted to discover more about Kaggle, the online community for machine learning students and data scientist practitioners then look no further than our expert-led guide to get an overview on all the basics you need to know about this amazing opportunity provider and competition organiser.
Many of you may have seen our State of Data Innovation report that we released recently; what better way to bring data and innovation closer together than through Machine Learning (ML)? In fact, according to this report, Artificial Intelligence (AI)/ML was the second most important tool for fueling innovation. So, naturally we have paired this report with a new release of the Machine Learning Toolkit (MLTK)!
Putting machines to work to enhance our everyday lives has been well-ingrained in our society for at least a couple of centuries now. IT workers use machine learning (ML) in their daily work routines, even if they don’t consciously realize it. Automated email alerts, issue escalations, and security patching are just a few examples of how ML has put the systems we rely on to work for us.
Machine learning has become an important component of many applications we use today. And adding machine learning capabilities to applications is becoming increasingly easy. Many ML libraries and online services don’t even require a thorough knowledge of machine learning. However, even easy-to-use machine learning systems come with their own challenges. Among them is the threat of adversarial attacks, which has become one of the important concerns of ML applications.
CPG executives invest billions of dollars in trade and consumer promotion investments every year, spending as much as 15-20% of their total annual revenues on these initiatives. However, studies show that less than 72% of these promotions don’t break even and 59% of them fail. Despite these troubling statistics, most CPG organizations continue to design and execute essentially the same promotions year after year with negligible hope of obtaining sustained ROI.
Many of you are familiar with Splunk’s Machine Learning Toolkit (MLTK) and the Deep Learning Toolkit (DLTK) for Splunk and have started working with either one to address security, operations, DevOps or business use cases. A frequently asked question that I often hear about MLTK is how to organize the data flow in Splunk Enterprise or Splunk Cloud.
