API latency in microservices - Trace based troubleshooting
In microservices architectures, apps are broken down into small, independent services that communicate with each other using APIs in a synchronous or asynchronous way.
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Chaos AI Assistant (CloudTrail Analysis)
Now you can actually have a conversation with your data! The Chaos AI Assistant is a breakthrough feature that elevates log and event data analytics. Seamlessly integrating with the ChaosSearch Platform, it utilizes AI and Large Language Models (LLMs), enabling you to talk to your data to unveil actionable insights.
ChaosSearch AI Assistant | Starting a Conversation With Your Data
Now you can actually have a conversation with your data! The Chaos AI Assistant is a breakthrough feature that elevates log and event data analytics. Seamlessly integrating with the ChaosSearch Platform, it utilizes AI and Large Language Models (LLMs), enabling you to talk to your data to unveil actionable insights.
Netdata & Ansible example: ML demo room
We are always trying to lower the barrier to entry when it comes to monitoring and observability and one place we have consistently witnessed some pain from users is around adopting and approaching configuration management tools and practices as your infrastructure grows and becomes more complex. To that end, we have begun recently publishing our own little example ansible project used to maintain and manage the servers used in our public Machine Learning Demo room.
Detecting Main Thread Issues in Mobile Applications
Mobile device users care about three things when it comes to good app performance: We’re going to look at how modern concurrency APIs can help with some of these. We recently shipped a new profiling feature to help you find the sources of main thread contention; specifically detecting issues with image and JSON decoding or regex matching. These point you to spots where you can immediately make improvements to your app’s UI performance.
Chatbots vs. Conversational AI: What's the Difference?
Chatbots are computer programs designed to simulate human conversations through textual or auditory means. They are typically rule-based and follow predefined scripts to respond to user inputs. While chatbots excel at providing basic information and handling simple inquiries, they often lack true conversational abilities and struggle to understand complex user intents. Examples of companies utilizing chatbots include customer support bots on websites and messaging platforms.
How Technological Advancements Transform Hiring Processes and Decisions
Technology has forever changed the landscape of businesses, especially in terms of HR practices. From hiring to onboarding and beyond, technological advancements have drastically altered the way companies develop their teams and make decisions. Automated technologies can help streamline processes, improve accuracy around evaluating job candidates, simplify communication and collaboration between colleagues globally-the list goes on! To better understand how technology influences human resources today and into the future, let's take a closer look at how far it has come already in transforming different stages within the typical hiring process.
API update: Manage tracked deployments
Today, we’re thrilled to announce the latest endpoints for the Raygun API – Deployments. This new release allows developers to efficiently manage deployments, with increased flexibility and control over their Raygun platform.
8 Important Things You Should Know About The Tech Used In The Food Industry
Are you curious about the technology that powers the food industry? From farm to fork, the use of cutting-edge tech has revolutionized how we produce, process, and consume our favorite meals. Whether you're a food enthusiast, a health-conscious individual, or simply intrigued by the latest innovations, understanding the role of technology in the food industry is vital. In this blog post, we'll explore eight important things you should know about the tech used in the food industry.
ML Observability: what, why, how
Note: This post is co-authored by Simon Aronsson, Senior Engineering Manager for Canonical Observability Stack. AI/ML is moving beyond the experimentation phase. This involves a shift in the way of operating because productising AI involves many sophisticated processes. Machine learning operations (MLOps) is a new practice that ensures ML workflow automation in a scalable and efficient manner. But how do you make MLOps observable?