We're excited to announce that autoscaling is now available on Elastic Cloud. In our initial release, autoscaling monitors the storage utilization of your Elasticsearch data nodes and the available memory capacity for your machine learning jobs.
It is incredibly useful to be able to identify the most unusual data in your Elasticsearch indices. However, it can be incredibly difficult to manually find unusual content if you are collecting large volumes of data. Fortunately, Elastic machine learning can be used to easily build a model of your data and apply anomaly detection algorithms to detect what is rare/unusual in the data. And with machine learning, the larger the dataset, the better.
AI is everywhere. Except in many enterprises. Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail, and for the few models that are ever deployed, it takes many months to do so. While AI has the potential to transform and boost businesses, the reality for many companies is that machine learning only ever drips red ink on the balance sheet.
Security is an essential part of any modern IT foundation, whether in smaller shops or at enterprise-scale. It used to be sufficient to implement rules-based software to defend against malicious actors, but those malicious actors are not standing still. Just as every aspect of IT has become more sophisticated, attackers have continued to innovate as well. Building more and more rules-based software to detect security events means you are always one step behind in an unsustainable fight.
According to the Association of Certified Fraud Examiners, the money lost by businesses to fraudsters amounts to over $3.5 trillion each year. The ACFE's 2016 Report to the Nations on Occupational Fraud and Abuse states that proactive data monitoring and analysis is among the most effective anti-fraud controls.
Here at Splunk we’re passionate about helping our customers get as much value from their data as possible. Recently Lila Fridley has written about how to select the best workflow for applying machine learning and Vinay Sridhar has provided an example of anomaly detection in SMLE.
I have seen many junior data scientists and machine learning engineers start a new job or a consulting engagement for a telecom company coming from different industries and thinking that it’s yet another project like many others. What they usually don’t know is that “It’s a trap!”. I spent several years forging telecom data into valuable insights, and looking back, there are a couple of things I would have loved to know at the beginning of my journey.
The data center is a remarkably complex structure. However, they are crucial to the everyday running of even the smallest businesses and enterprises. Whether in-house, cloud, or hybrid, the average data center management requires specialist knowledge and meticulous oversight for max efficiency. That is one reason, at least, why machine learning is emerging as an ideal partner for centers of the future.
Some of you may have seen recently that we are trying to commoditize machine learning through our MLTK smart workflows. Here I’d like to outline another example of an MLTK smart workflow, designed to help improve the usability of the predictive capabilities in ITSI.
