MLOPS
30 days · UTC
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From Pilot Purgatory to Platform: Shipping AI That Actually Works
Many AI pilots are stuck as demos; production success needs a real platform, guardrails, and workflow automation. Analyses flag a widening execution ...
Optimizer choice can make or break model retention in continual training
A HackerNoon piece argues that your optimizer can heavily influence how much a model forgets during continual training. A recent [HackerNoon article]...
Make catastrophic forgetting a first-class metric in your ML pipeline
A HackerNoon article explains how to measure catastrophic forgetting in AI and flags optimizer choice as a likely driver of retention issues. The pie...
Agentic AI needs a control plane to survive production
Agentic AI proofs-of-concept often crumble in production; a control plane with guardrails and visibility can make them dependable.
Shipping AI is ops, not notebooks: a practical MLOps blueprint
A hands-on blueprint shows how to run AI systems reliably using containers, a registry, and multi-service orchestration.
Runpod data: Qwen just passed Llama as the most-deployed self‑hosted LLM
Runpod’s latest platform data says Qwen has overtaken Llama as the top self-hosted LLM. According to Runpod’s report, more teams now spin up Qwen tha...
Golden sets and real-time scoring: patterns for trustworthy AI pipelines
Three recent pieces outline how to build trustworthy AI decision systems by combining golden-set evaluation, calibrated real-time scoring, and reliabl...
When AI Shipping Outpaces Governance: A $500K Lesson
A case study shows a team staffed 8 engineers for AI implementation and 0 for governance, leading to a $500K mistake. The core miss was failing to ass...
AI architecture for banks: agentic execution, contextual data, safety-by-design
A recent banking-focused blueprint argues the bottleneck is not the model but the architecture around it. It recommends agentic AI for outcome-aligned...