Tag: MLOps
DevOps for GenAI Toronto Edition Hackathon: Unlocking New Opportunities
This week in Toronto, at the DevOps for GenAI Hackathon, something remarkable occurred: Industry professionals, academic leaders and learners representing top academic institutions, fintech, technology firms, and consulting companies came together in ...
SRE in the Age of AI: What Reliability Looks Like When Systems Learn
Muhammad Yawar Malik | | adaptive systems, AI observability, AI reliability, concept drift, data pipeline reliability, feedback loops, incident response, learning systems, ML operations, MLOps, model drift, model performance monitoring, reliability engineering, site reliability engineering in AI, SREAs AI and ML become core production components, SRE is evolving from managing deterministic systems to ensuring the reliability of dynamic, learning systems. New metrics, workflows, guardrails and cross-disciplinary practices are redefining ...
DevOps for GenAI Toronto Edition Hackathon: Unlocking New AI Market Opportunities
Toronto’s vibrant tech community is abuzz as the DevOps for GenAI Hackathon returns with John Willis, this time with ambitious new themes that reflect both global trends and uniquely Canadian innovation. Building ...
JFrog Adds Ability to Track Usage of AI Coding Tools
Mike Vizard | | AI API management, AI code auditing, AI coding tools, AI compliance tracking, AI Governance, AI in DevOps, AI model inventory, AI risk mitigation, AI-generated code, application security, code risk detection, devsecops, Large Language Models, LLM security, MLOps, SCA, secure coding practices, Shadow AI detection, software composition analysis, software provenance, Software Supply Chain Security, software vulnerabilities, source code scanningJFrog introduces AI-Generated Code Detection and Shadow AI Detection tools to identify AI-created code, track model usage, and enhance DevSecOps governance across software supply chains ...
From Code to Confidence: Building AI Apps That Earn User Trust
Chris Sheehan | | AI bias, AI ethics, AI Governance, AI quality assurance, AI testing, AI trust, Applause, bias detection, explainable AI, fairness, feedback loops, human-centered AI, inclusive testing, MLOps, model transparency, responsible AI, trustworthy AI, user confidence, user experienceAs 65% of users report issues with AI applications, trust has become the new UX battleground. Learn how developers can build fair, transparent, and reliable AI systems through human-centered testing, inclusive feedback ...
Whose Ops is it Anyway? How IDPs, AI and Security are Evolving Developer Culture
Olivier de Turckheim | | AI and ML workloads, AI in DevOps, AI infrastructure, AI-driven operations, AIOps, automation in DevOps, continuous delivery, Cycloid plug-ins, developer experience, devops culture, devops evolution, DevOps scalability, DevOps security integration, DevOps transformation, devsecops, digital transformation DevOps, gitops, IDP, intelligent automation, internal developer platforms, MLOps, modern DevOps strategy, operational resilience, platform engineering, platform governance, secure CI/CDIt was only a few years ago that perhaps the biggest hurdle for DevOps advocates was convincing leadership that it was worth the investment. That conversation has since shifted. In most organisations ...
MLOps at Scale: How Community Is Driving AI Into Production
At swampUP 2025, Alan caught up with Demetrios Brinkmann, founder of the MLOps Community, to discuss the growing gap between AI research and real-world production deployments. Brinkmann leads a global network of ...
AI‑First DevOps is Becoming the Default
AI-first DevOps is fast becoming the firm default and is now seen as the operational baseline rather than an experimental trend ...
MLOps for Green AI: Building Sustainable Machine Learning in the Cloud
Enter MLOps for Green AI: A fusion of machine learning operations (MLOps) and sustainable DevOps practices that promise to make AI not just smarter, but greener. ...
Focus on Basics or Innovation? DevOps Success Requires Both
Walter McAdams | | Adaptive Builds, ai, CI/CD, devops, infrastructure as code, MLOps, site reliability engineeringDevOps teams are experiencing a chasm between traditionalists and innovators — a rift that grows as pressure mounts for teams to leverage the latest advancements. Traditionalists are fighting to preserve the basics, ...
DevOps for Machine Learning and Artificial Intelligence
DevOps for machine learning and artificial intelligence, known as MLOps, evolves to meet the demands of the AI landscape ...
JFrog Acquires Qwak to Meld MLOps and DevOps Workflows
JFrog today revealed it has acquired Qwak to add a machine learning operations (MLOps) platform to its existing portfolio of DevOps tools and platforms ...
