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)!

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.

At ElasticON Global 2021, we shared a future view of Elastic Enterprise Search and how we’re continuing to build next-generation, machine learning-powered search experiences backed by the speed, scale, and relevance of Elasticsearch. We also highlighted the many ways we plan to keep building even more flexibility into our solutions.

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.

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.

As soon as the ITOps technician is ready to grab a cup of coffee, a zing comes along as an alert. Cling after zing, the technician has to respond to so many alerts leading to fatigue. The question is why can’t systems be smart enough to predict bugs and fix them before sending an alert to them. And, imagine what happens when these ITOps personnel have to work with a complex and hybrid cloud of IT systems and applications. They will dive into alert fatigue.

The artificial neuron was first hypothesized in the 1930s, but only in the last decade have we seen the widespread application of artificial neural networks and machine learning to everyday technologies. Broadly speaking, machine learning describes a technical discipline defined by computer algorithms that improve automatically through experience and the use of data. These days, the combination of machine learning and "big data" power an increasing number of digital tools that we interact with daily.