Tag: machine learning
The Future of Observability: Predictive Root Cause Analysis Using AI
Karthikeyan Uthirapathy | | AI in DevOps, AI observability, AI-driven RCA, AI-powered monitoring, anomaly correlation, anomaly detection, artificial intelligence, automated incident response, cloud environments, devops, distributed systems, intelligent observability, kubernetes, logs metrics traces, machine learning, microservices, observability tools, Predictive Analytics, predictive monitoring, predictive root cause analysis, proactive observability, rca, self-healing systems, system reliabilityIn the past few years, systems have become more complex than ever. Microservices, Kubernetes, cloud environments and distributed application programming interfaces (APIs) have changed how we build and manage software. However, this complexity has also made it harder ...
The AI-Powered Evolution of Software Development
Vishwanath Krishnamurthy | | agentic AI, AI for developers, AI in DevOps, AI in software development, AI productivity tools, AI testing tools, AI-driven coding, AI-native applications, AI-powered development, AI. analytics, autonomous software operations, code optimization, devex, Future of Software Engineering, GitHub Copilot, intelligent automation, machine learning, OpenAI Codex, predictive maintenance, software qualityArtificial intelligence is revolutionizing software development—accelerating coding, improving quality, and enabling autonomous operations. From GitHub Copilot to AI-driven DevOps, businesses leveraging AI tools are building smarter, faster, and more adaptive applications ...
Is There Still a Difference Between DevOps and AIOps?
Alexander Williams | | AI in IT operations, AIOps, automated remediation, CI/CD, devops, digital transformation, intelligent automation, machine learning, observability, Predictive AnalyticsDevOps transformed software delivery through speed and collaboration. AIOps takes it further—adding AI-driven insight, anomaly detection, and intelligent automation. Together, they form the next evolution of IT operations: smart, adaptive, and self-healing ...
How Software Engineers and Students Use AI to Move Faster than Ever (Without Breaking Things)
Vivek Vaidya | | agentic AI, AI adoption, AI and students, AI coding tools, AI collaboration, AI engineers, AI evolution, AI in education, AI in software development, AI in universities, AI innovation, AI revolution, AI skills, AI skills gap, AI startups, AI Tools for Developers, AI transformation, AI workforce adaptation, AI-native companies, AI-native design, AI-native learning, AI-powered development, AI/ML, artificial intelligence, augmented development, augmented intelligence, Cursor, developer productivity, digital transformation, Enterprise AI, future of work, human-AI collaboration, LLMs, machine learning, super{set}, VC investment in AIWe’re living through the AI revolution — or more accurately, the age of augmented intelligence. From agentic AI tools transforming software development to AI-native education shaping the next generation, super{set} explores how ...
Grafana Labs Looks to Make Composing Observability Platforms Simpler
Mike Vizard | | data, Grafana Labs, load testing, low-code tools, machine learning, monitoring toolsGrafana Labs, today at its ObservabilityCon event, unfurled a raft of additional offerings, including public previews of Explore Traces and Explore Profiles tools that make it simple to drill down into data ...
Transforming DevOps With AI: Practical Strategies To Supercharge Your Workflows
Despite the hype, AI can benefit actual DevOps workflows. These four machine-learning algorithms can make a real difference ...
Building an Open Source Observability Platform
Prashant Nerkar | | AIOps, machine learning, monitoring, observability, site reliability engineering, tech innovationBy investing in open source frameworks and LGTM tools, SRE teams can effectively monitor their apps and gain insights into system behavior ...
Machine Learning in Predictive Testing for DevOps Environments
The integration of AI and ML in testing is a fundamental shift in how we approach software quality and reliability in DevOps environments ...
Applying AI/ML to Continuous Testing
Artificial intelligence (AI) and machine learning (ML) can play a transformative role across the software development lifecycle, with a special focus on enhancing continuous testing (CT). CT is especially critical in the ...
JFrog Extends MLOps Integration Efforts via Qwak Alliance
JFrog's integration with Qwak's MLOps platform will advance collaboration between teams building and deploying multiple classes of software artifacts ...
6 Transformative Mainframe Predictions for 2024
John McKenny | | AIOps, application, artificial intelligence, automation, data management, devops, enterprise, machine learning, mainframe, MLOpsThe success of organizations in today’s digital economy demands speed — the ability to quickly respond to market trends with new applications and services, instant access to critical data, and resolution of ...
Will Generative AI Succeed Where AIOps Failed?
Generative AI can go so many places that AIOps could never, and provides a general-purpose approach that can be applied in many different ways ...
