Self-Hosted Databricks Alternative: Homelab Data Lakehouse (AI/ML Data Factory)
Self-Hosted Databricks Alternative: Homelab Data Lakehouse Part of the homelab build series. Trino, Apache Iceberg, Project Nessie, dbt, Spark, MLfl…
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MLOps: models that survive production · 2. Data versioning
MLOps: models that survive production · Chapter 2 of 12 · AI Engineering · new chapter every Thursday night By the end of this chapter: Reproduce a…
How Accurate Is Your Model Right Now? Estimating Accuracy Without Labels
Label-free accuracy estimation under data shift — and the monitoring sidecar that uses it. Scope note: two intent-classification datasets (Banking77…
Your AI Code Can Be Elegant Too
As the Machine Learning Engineering Manager at an AI-powered SaaS company, I get a front-row seat to the machine learning (ML) code written by data…
My drift board learned to say "I don't know". Here is what it still cannot say.
Archival copy of record, committed before this post: https://github.com/Tania-coder/SEISMOGRAPH/blob/main/docs/reports/2026-09-22-weather-report-02.…
Moving Your Local Airflow to GCP for under $150 a month
My team runs ML training and prediction pipelines on Vertex AI. For a long time, the thing telling those pipelines when to run was Apache Airflow, i…
Owning vs. Renting Intelligence: Why Enterprises Are Building Sovereign AI
When Mythos went dark in June 2026, the reaction across the tech ecosystem wasn’t just disappointment. It was a collective chill down the spine of e…
CI said the deploy succeeded
There is a moment in every incident where someone asks "wait, is the fix even deployed?" and the room goes quiet. Somebody pulls up the CI dashboard…
Mastering LLM-as-Judge: Automated Annotation and Triage for Production AI Failures
Mastering LLM-as-Judge: Automated Annotation and Triage for Production AI Failures Every single time you push a new system prompt or swap out an und…
Deploying Kubeflow as an Azure ML Alternative
Azure Machine Learning is Microsoft's cloud-native machine learning platform that provides experiment tracking, managed compute, pipelines, a model…
MLOps Best Practices 2026
MLOps Best Practices 2026 {"title": "MLOps Best Practices 2026: Scaling AI from Prototype to Enterprise Production", "content": "### Introduction\n\…
Why LLM Cascades Fail for Interactive Apps — Use Router
Stop Defaulting to Cascades: Why Router-First Wins for Interactive Apps (LLM routing vs cascading) The question of "LLM routing vs cascading" is not…
Building a Production-Grade End-to-End MLOps Pipeline from Scratch
Building a Production-Grade End-to-End MLOps Pipeline from Scratch Most machine learning tutorials end at model.fit() . But in the real world, train…
18 Insights from Mass-Producing Voice Models — From Diffusion TTS Voice Design to Training Corpus Creation and Quality Gate Pitfalls
📝 Originally published (in Japanese) at forge.workstyle.tech . This is a record of designing voices from single-line captions, automatically creatin…
Backtest 63.7%, production AUC 0.51 - a postmortem
Originally published at nbaproplab.com/learn/model-postmortem . A model that looks excellent offline and random in production is usually blamed on t…
How to Build an Enterprise-Grade, Automated MLOps Pipeline on AWS
A comprehensive blueprint for orchestrating continuous training, governance, and canary deployments with automated rollbacks. Executive Summary &…
Migrating to Native Git in SageMaker Unified Studio
Version control in analytical data platforms has always been a second-class citizen — tolerated, not designed. SageMaker Unified Studio just changed…
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…
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…