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.
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.
Machine learning (ML) and analytics make data actionable. Without it, data remains an untapped resource until a person (or an intelligent algorithm) analyzes that data to find insights relevant to addressing a business problem. For example, amidst a network outage crisis a historical database of network log records is useless without analysis. Resolving the issue requires an analyst to search the database, apply application logic, and manually identify the triggering series of events.
Measuring your machine learning model will help you understand how well your model is doing, how useful it is, and whether your model can perform better with more data. This is what Algorithmia Insights — a feature of Algorithmia Enterprise MLOps platform — does. Algorithmia platform accelerates your time to value for ML by delivering more models quickly and securely, as it is estimated that 85% of machine learning models never make it to production.
Modern IT & DevOps teams face increasingly complex environments — making it harder to quickly detect and resolve critical issues in real-time. To overcome this challenge, Splunk users can take advantage of ML-powered IT monitoring and DevOps solutions available in a scalable platform with state-of-the-art data analytics and AI/ML capabilities. In this blog, we deploy Splunk’s built-in Streaming ML algorithms to detect anomalous patterns in error logs in real-time.
It is with great excitement that we announce our first-ever supervised ML and security integration! Today, we are releasing a supervised ML solution package to detect domain generation algorithm (DGA) activity in your network data. In addition to a fully trained detection model, our release contains ingest pipeline configurations, anomaly detection jobs, and detection rules that will make your journey from setup to DGA detection smooth and easy.
With the speed of change in artificial intelligence (AI) and big data, podcasts are an excellent way to stay up-to-date on recent developments, new innovations, and gain exposure to experts’ personal opinions, regardless if they can be proven scientifically. Great examples of the thought-provoking topics that are perfect for a podcast’s longer-form, conversational format include the road to AGI, AI ethics and safety, and the technology’s overall impact on society.
