Machine Learning

Improve MTTR by Using Machine Learning for Alerts

Did you know Freshservice can help reduce noise by up to 50% using ML algorithms? Watch our video to learn how Freshservice uses machine learning to translate the swarm of signals from the monitoring tools into stories you can act upon fast. Join us to explore our latest ITOM features to break the silos in your processes. #letsTalkITOM

Building a Real-Time ML Pipeline with a Feature Store - MLOps Live #16

With the growing business demand for real-time use cases such as NLP, fraud prediction, predictive maintenance and real-time recommendations, ML teams are feeling immense pressure to solve the operational challenges of real-time feature engineering for machine learning, in a simple and reproducible way. This is where online feature stores come in. An online feature store accelerates the development and deployment of online AI applications by automating feature engineering and providing a single pane of glass to build, share and manage features across the organization.

Using Automated Model Management for CPG Trade Success

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.


Go with your Data Flow - Improve your Machine Learning Pipelines

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.


All That Hype: Iguazio Listed in 5 Gartner Hype Cycles for 2021

We are proud to announce that Iguazio has been named a sample vendor in five 2021 Gartner Hype Cycles, including the Hype Cycle for Data Science and Machine Learning, the Hype Cycle for Artificial intelligence, Analytics and Business Intelligence, Infrastructure Strategies and Hybrid Infrastructure Services, alongside industry leaders such as Google, IBM and Microsoft (who are also close partners of ours).

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When Dominoes Fall: Microservices and Distributed Systems need intelligent dataops and AI/ML to stand up tall

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.