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.

‍As children we believed in magic, imagined, and a fantasy where robots would one day follow our commands, undertaking our most meager tasks and even help with our homework at the push of a button! But sadly it always seemed that these beliefs, along with the idea of self-driven aero cars and jetpacks, belonged in a future beyond our imagination or in a Hollywood Sci-fi. Would we ever get to experience the future in our lifetime?

Logz.io is releasing its AI-powered Exceptions, a revamped version of our Application Insights, fully embedded in your Kibana Discover experience, to boost your troubleshooting experience and help you find bugs in the log haystack.

Splunk is a machine data platform with advanced analytic capabilities that allows anyone to get valuable insights from their data. With unlimited use cases, you can leverage SPL to run any analytics you want. SPL has been supporting native machine learning capabilities for some time now. All you have to do is install the Splunk Machine Learning Toolkit (MLTK) and you are good to start predicting !

Kubeflow, the ML toolkit on K8s, now fits on your desktop and edge devices! 🚀 Kubeflow provides the cloud-native interface between Kubernetes and data science tools: libraries, frameworks, pipelines, and notebooks. > Read more about what is Kubeflow To make Kubeflow the standard cloud-native tool for MLOps within the AI landscape, the open-source community has accomplished the aggregation and integration of many projects on top of Kubernetes.

As data continues to explode across the enterprise, we are finding that it is becoming increasingly challenging for organizations to keep up. A recent Splunk report, "The Data Age is Here," found that 57% of companies interviewed expressed that the volume of data is growing faster than they can manage, with 47% bluntly saying they will fall behind when faced with rapid data volume growth.

Canonical, the publisher of Ubuntu, releases Charmed Kubeflow, a set of charm operators to deliver the 20+ applications that make up the latest version of Kubeflow, for easy consumption anywhere, from workstations to on-prem, public cloud, and edge. > Visit Charmed-kubeflow.io to learn more. Kubeflow provides the cloud-native interface between Kubernetes, the industry standard for software delivery and operations at scale, and data science tools: libraries, frameworks, pipelines, and notebooks.