Deploying ClearML as a GCP Vertex AI Alternative on Ubuntu
Google Vertex AI is Google Cloud's managed ML platform with experiment tracking, training jobs, pipelines, a model registry, and endpoints, but it loc…
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Can your AI agent actually manage ML infrastructure?
I’ve spent enough time in production environments to know that 'chatting with an AI' is a useless metric if the AI can't touch the actual hardware or …
Summary — Your Next Steps as an AI Architect
What We Built in This Guide In the previous guide, we went from RAG to cloud deployment. In this guide, we systematically implemented everything neede…
Three weeks before the enterprise contract, the voice agent wasnt operator-ready.
Three weeks before the enterprise contract, the voice agent wasn't operator-ready Look. We had 99.2% uptime in staging. We had eval coverage on 1,400 …
Deploying ClearML as an AWS SageMaker Alternative on Ubuntu
ClearML is an open-source MLOps platform that pairs experiment tracking, pipelines, hyperparameter optimisation, and model serving, a self-hosted alte…
«Взрыв поверхности» или «вертикальный срез»: какой подход выбрать при построении MLOps-платформы?
Привет, Хабр! Меня зовут Кирилл Кулаков, я занимаюсь развитием MLOps-платформы в Uzum Fintech. Недавно у нас в команде разгорелся спор о том, как прав…
MLOps for LLM: A Case Study on Dresscode
I've recently participated in the Gemma 4 challenge here on DEV.to, but fell short compared to many amazing projects. I really liked LIKAS . I encoura…
Never lose a training run again: a checkpoint-and-resume playbook for ephemeral GPUs
▶ Prefer to play with it? There's an interactive version of this article where you can break things yourself: https://resumable-ml-training.vercel.app…
The SDXL VAE overflow that decoded black images in fp16
TL;DR: The SDXL VAE decoder pushes activations past 65504, the max value fp16 can hold, so the last decode step overflows to inf and you get a fully b…
Semantic caching our flaky-test summariser: 58% fewer LLM calls
TL;DR: Our internal flaky-test summariser at Buildkite was firing ~40k LLM calls a day, and most were near-duplicates of failures we'd already explain…
Speculative decoding shifted our output distribution and evals missed it
TL;DR: We turned on speculative decoding in vLLM to cut latency on a fine-tuned 8B. Got a 1.9x throughput win. Three weeks later a customer flagged th…
Winograd convolutions cost us 2 mAP and we didn't notice for a month
TL;DR: We turned on Winograd convolution to shave latency off a pedestrian detector running on a Cortex-A53, got a clean 18% speedup, and silently los…
From ML Tooling to Analytical Governance: Recent Updates to KMDS
Over the last few months I've been refining KMDS, a framework for building repeatable and auditable machine learning systems. The original motivation …
Why multi-agent orchestration is harder than it looks
One AI agent answering a question is useful. Five agents that divide a complex task, pass state to each other, and act on live enterprise systems is a…
Quantization formats compared: GGUF vs GPTQ vs AWQ vs NF4
Quantization formats compared: GGUF vs GPTQ vs AWQ vs NF4 You just finished fine-tuning a 7B parameter model. The raw FP16 weights are 14 GB. Your tar…
I Built a Production RAG System on My M1 Mac for $0
I Built a Production RAG System on My M1 Mac for $0 Most RAG tutorials stop at "it answers questions." But answering questions is table stakes. The re…
Миф о «равных весах»: что на самом деле скрывается внутри малых моделей
Последние годы развитие LLM шло по пути экстенсивного масштабирования: считалось, что чем больше весов и данных, тем умнее модель. В индустрии даже сл…
Our event-camera detector lost 6 mAP to a badly chosen accumulation window
TL;DR: We spent three weeks chasing a 6 mAP regression in an event-camera object detector. The model was fine. The bug was the accumulation window we …
I Built a Complete AI Infrastructure Stack from Scratch — Here's What I Learned
I Built a Complete AI Infrastructure Stack from Scratch — Here's What I Learned Most AI projects start at the top of the stack. You grab an LLM API, w…
QAT vs PTQ on our edge vision model: 6 months of A/B data
TL;DR: We ran post-training quantisation (PTQ) and quantisation-aware training (QAT) side by side on the same defect-classification model deployed on …
Part 2: Enterprise Decision Intelligence Architecture: AI Governance, Threshold Policy Engines, and Operational AI Systems
Part 1 showed how to evaluate binary classification thresholds in Python. This part asks the harder enterprise question: What happens when that thresh…
Why 91% of AI Agents Fail in Production (And What the 9% Do Differently)
Everyone is building AI agents right now. Autonomous systems that reason, plan, and act without humans in the loop. Agents that write code, manage wor…
llm-nano-vm v0.8.0 — deterministic FSM runtime for LLM pipelines, now with output validation and per-step timeouts
PyPI: pip install llm-nano-vm GitHub: http://github.com/Ale007XD/nano_vm MCP gateway: http://github.com/Ale007XD/nano-vm-mcp I've been building a dete…
Stop paying for idle GPUs in your CI: batching LLM eval jobs
TL;DR: Running LLM evaluations on every PR will burn your GPU budget faster than you can blink. We cut our eval spend by about 60% by batching jobs in…
What GenAI Actually Costs in Production
The first number anyone quotes when asked what generative AI costs is a per-token figure. It is a comfortable number — small, unambiguous, available o…
The Missing Engineering Stack for Production AI Agents
The "build an agent in 5 minutes" tutorials get you to a demo. They don't get you to production. Here's the field guide for the four primitives that d…
How we catch silent NPU fallback on Snapdragon in CI (and why your eval set won't)
TL;DR — ONNX Runtime's QNN execution provider will quietly route unsupported ops to the CPU instead of the Hexagon NPU. Your accuracy is fine. Your ev…
Beyond Monitoring: Building AI-Powered Predictive Observability for Retail Data Pipelines published
Three numbers before we start: Average detection time with traditional monitoring: 4.2 hours Average detection time with predictive observability: 11 …