Operations | Monitoring | ITSM | DevOps | Cloud

With the precision of search and the intelligence of AI, Cisco uses Elastic on Google Cloud to create richer search experiences, so support engineers can quickly find the answers they need. Scaling from this success, Cisco's Search team added AI models, semantic search, and vector search to more than 50 internal- and external-facing apps, helping them innovate more quickly and increase overall operational efficiency.

We’re excited to announce Vectorize now integrates with Elasticsearch vector database! This powerful combination simplifies building retrieval augmented generation (RAG) pipelines, allowing AI engineers to focus on building applications with unprecedented speed and accuracy. Elasticsearch vector database enables fast and efficient real-time search and retrieval of vector data, making it an excellent database for RAG applications.

Understanding telemetry signals for better decision-making, improved performance, and enhanced customer experiences Telemetry signals have evolved significantly over the years — if you blinked, you could have missed it. In fact, much of the common wisdom about observability needs a refresh. If your observability solution doesn’t consider the current state of telemetry, you might need an upgrade.

The combination of AI and search enables new levels of enterprise intelligence, with technologies such as natural language processing (NLP), machine learning (ML)-based relevancy, vector/semantic search, and large language models (LLMs) helping organizations finally unlock the value of unanalyzed data. Search and knowledge discovery technology is required for organizations to uncover, analyze, and utilize key data.

How Proficio leveraged Elastic Security on AWS to revolutionize threat detection and response In today’s rapidly evolving digital landscape, maintaining robust cybersecurity defenses has never been more critical. Proficio, a leading managed security services provider, faces the continual challenge of monitoring an expansive array of data points and potential vulnerabilities.

Achieving modern observability with a unified data platform and Search AI If you have a love-hate relationship with your data, we don’t blame you. It’s generated at high velocity and from all sides — your apps, endpoints, networks, and servers. By 2025, global data creation is projected to grow by more than 180 zettabytes.* Inside this wealth of data lies better operational resilience, profitability, and innovation.

Since the release of ChatGPT in November 2022, companies have either banned or rushed to adopt generative artificial intelligence (GenAI), which is rapidly expanding in use and capabilities. Its powerful yet unpredictable nature poses significant cybersecurity risks, transforming it into a double-edged sword. However, whether generative AI poses more opportunity or risk is not necessarily the right question to ask.

With the upcoming release of JDK 23, there are some significant changes in locale information that will affect Elasticsearch and how you ingest and format datetime data. Firstly, a bit of background.

It’s 10:00 p.m. on a Sunday when my 9th grader bursts into my room in tears. She says she doesn’t understand anything about algebra and is doomed to fail. I jump into supermom mode only to discover I don’t remember anything about high school math. So, I do what any supermom does in 2024 and head to ChatGPT for help. These generative AI chatbots are amazing. I quickly get a detailed explanation of how to solve all her problems.

The latest LM Studio 0.3 update has made Elastic’s AI Assistant for Security run with an LM Studio-hosted model easier and faster. In this blog, Elastic and LM Studio teams will show you how to get started in minutes. You no longer need to set up a proxy if you work on the same network or locally on your machine.