I’m excited to announce the launch of a new series of apps on Splunkbase: MLTK Smart Workflows. These apps are domain-specific workflows, built around specific use cases, that can be used to help you develop a set of machine learning models with your data. In this blog post, I’d like to take you through the process we adopted for developing the workflows.

While working with customers over the years, I've noticed a pattern with questions they have around operationalizing machine learning: “How can I use Machine Learning (ML) for threat detection with my data?”, “What are the best practices around model re-training and updates?”, and “Am I going to need to hire a data scientist to support this workflow in my security operations center (SOC)?” Well, we are excited to announce that the SplunkWorks team launched a new add-

Project owners and developers turn to open source APM tools to lessen the cost of application performance monitoring. In this entry, let’s examine the attributes of these open source tools. Years ago, traditional APM solutions were designed for IT only, particularly network operations. The APMs were used to monitor data to ensure the network’s Quality of Service(QoS). However, the landscape has changed.

Machine learning pipelines have evolved tremendously in the past several years. With a wide variety of tools and frameworks out there to simplify building, training, and deployment, the turnaround time on machine learning model development has improved drastically. However, even with all these simplifications, there is still a steep learning curve associated with a lot of these tools. But not with Elastic.

Catchpoint has always embraced new technologies and ideas. We offer a powerful monitoring platform with advanced features such as tracking digital performance from across the globe, capturing analytical data and the ability to get notified across various channels. With all these inbuilt features in hand, Catchpoint encourages its customers to build new monitors and integration that consumes monitoring data that are tailored to specific use cases.

Legacy generations of networks have predominately provided services with best-effort delivery. While this has worked for voice, text and best- effort broadband services, end users are hampered with buffering, delays, and drops as the demand for feature-rich services continues to grow.