How I Self-Host vLLM on Cloud GPUs for Sub-180ms Inference (And Saved 45% on Costs)
When building 24/7 autonomous daemon agents and real-time LLM-driven game loops , API bills from commercial LLM providers explode fast. If your agen…
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LLMOps for RAG Systems — Production Checklist
Why the pipeline, not the model, is usually to blame If your LLM feature starts hallucinating or your inference bill doubles overnight, the model is…
Four bugs from building a platform where AI agents publish autonomously
I spent six weeks building a publication where AI agents write articles, an automated moderator approves or rejects them, and no human reviews anyth…
When Should You Retrain Your ML Model? A New Study Says Most Teams Are Solving the Wrong Problem
Every production ML model starts degrading the moment it ships. Customer behavior shifts, fraud patterns evolve, market conditions change — the tech…
The Serverless Equation: Conquering the Cold Start in Real-Time AI Inference
In our inaugural issue , we established that the future of enterprise AI lies not merely in raw model parameters, but in the architectural paradigms…
I Reviewed 100 Reddit Threads About GPU Clouds. Price Was Only Part of the Story.
When engineers compare GPU clouds, the conversation usually starts with three familiar numbers: GPU model VRAM Hourly price Those numbers matter. Bu…
Constrained Decoding vs Post-hoc Validation: Production LLM Extraction Needs Both
Constrained decoding and post-hoc validation solve different problems. Constrained decoding is generation-time control: fewer malformed payloads les…
You're Not Paying for Compute. You're Paying for Memory Bandwidth
TL;DR— Inference cost conversations obsess over FLOPs and token prices, but the real constraint on LLM serving is memory bandwidth— specifically the…
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 l…
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 o…
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 nee…
Your Guardrails Are a Firewall. Your Failures Are a Cascade
TL;DR— Most production AI teams build safety layers using the content-moderation mental model: classify input, classify output, block or pass. But t…
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 1,40…
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 al…
Deploying Kubeflow as an AWS SageMaker Alternative
Kubeflow is the open-source MLOps platform for Kubernetes, a self-hosted alternative to AWS SageMaker that bundles JupyterLab notebooks, KFP pipelin…
Azure Databricks for MLOps and Feature Engineering at Scale with Apache Spark, Delta Lake, and MLflow
Raw data doesn't win model competitions. Features do. And when your raw data is tens of billions of rows sitting across multiple sources, you can't…
What Is an Agent Registry? (And What We Broke Before We Had One)
TL;DR An AI agent registry is a centralized catalog of every agent in your organization — what each agent does, what tools it can access, what versi…
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 encou…
Stop building custom wrappers for your ML models.
I spent three days last month building a specialized API wrapper for a simple Scikit-learn model. Not because the logic was hard—it wasn't. Because…
Channels-last memory format cut our conv backbone latency 22%
TL;DR: Switching our convolutional segmentation backbone to PyTorch's channels-last memory format cut inference latency by about 22% on A100s, with…
Deploying MLflow Open-Source Machine Learning Experiment Tracking on Ubuntu 24.04
MLflow is an open-source platform for managing the machine learning lifecycle — experiment tracking, model registry, and reproducible runs. This gui…
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.a…
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…
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 expla…
Data Contracts in Production: Stop Trusting Your Upstream Sources
Your upstream data source changed a column type last night. Your pipeline ran at 2am, ingested everything without a single error, and by the time yo…
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…
AI Workloads Are Reshaping Kubernetes in 2026: GPU Scheduling, MLOps, and the Platform Engineering Reckoning
How GPU scheduling complexity and MLOps integration are forcing platform teams to rearchitect Kubernetes clusters before operational debt becomes in…
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 l…
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 motivatio…
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…