MLOps & LLMOps

An MLOps rollout decision is not “the candidate metric is higher, so ship it.” A defensible gate first refuses insufficient evidence, then protects hard reliabi

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Why most ML models die quietly in production Continue reading on Medium »

Why most ML models die quietly in production Continue reading on Medium »

When enterprises move algorithm models from development to production, the real challenge begins after the model is built. A model must navigate file packaging,

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A technical deep dive into Project 7 of my MLOps portfolio, an automated pipeline that retrains, evaluates, and redeploys a model with… Continue reading on Medi

A technical deep dive into Project 7 of my MLOps portfolio, an automated pipeline that retrains, evaluates, and redeploys a model with… Continue reading on Medi

Notes from an engineer building around models Continue reading on Medium »

Prefer to watch? The full video version is above. This is the reference-doc edition. Continue reading on Medium »

What actually separates “I trained a model” from “I can ship one” — and the free guide I built to close that gap Continue reading on Medium »

13,000 options. Here is how to pick the right ones. Continue reading on Medium »

The notebook is where ML is born. A modular Python project — with DVC, MLflow, and FastAPI stitched in — is where it grows up. Continue reading on Medium »

A conceptual introduction to operating machine learning systems — without tying it to any particular platform. Continue reading on Medium »

I joined a used-car export company as the only engineer. There was no existing codebase. No...

If you have ever shipped a modern application stack to a real cluster-a web UI, an API, background workers, a search engine, object… Continue reading on Medium

There is a new item on the DevOps job description in 2026, and most job postings have not caught up to naming it properly yet. It usually… Continue reading on M

How to architect a resilient, production-ready healthcare monitoring system using FastAPI, Streamlit, Evidently AI, Prometheus, Docker, and… Continue reading on

This article presents a phased blueprint for migrating from Google Workspace to Microsoft 365 with zero data loss and minimal downtime. It covers tenant hardeni

Why Most Production Models Fail Because Their Features Are Old, Not Because Their Models Are Bad Continue reading on Medium »

Today’s task was to build a complete DVC ML Pipeline with Remote Storage and Experiments. Continue reading on Medium »

How experience in critical infrastructure, conversations with HPC professionals, and AI-assisted engineering inspired the design of an… Continue reading on Medi

MLOps is not DevOps with a machine learning flavour. It solves a different class of problems — and the organisations that treat it like… Continue reading on Med

Running ML and LLM workloads on Kubernetes often starts with a surprisingly expensive step: getting the model onto the node. Continue reading on Medium »

Getting an AI API request to work is only the beginning. Once a product uses multiple models across...

Tired of simplified tutorials that show you how to run DeepSeek R1 on a personal laptop but leave...

MCP Health Check: Building Production Monitoring for Your MCP Server — What I Learned After 84 Production Outages Let me be honest with you — I've built 10+ MCP

Architect a robust MLOps pipeline from scratch using Python, Prefect, MLflow, and Flask to power real-time e-commerce tech. Continue reading on Analytics Vidhya

Most teams think about version control for code. Developers version: application...

Certified MLOps Professional is a training program aimed at helping professionals develop the competencies required to streamline the… Continue reading on Mediu

Five battle-tested patterns for building scalable, governed autonomous AI systems at enterprise scale Continue reading on Medium »

Five battle-tested patterns for building scalable, governed autonomous AI systems at enterprise scale Continue reading on Medium »

The Honest Story Two months ago, everyone was posting about Amazon S3 Files. New feature,...

If you’ve spent any time integrating AI into a real-world business workflow recently, you’ve probably...

Most AI models fail to reach production, but MLOps tools automate and scale the lifecycle to deliver real business impact.

Compare DevOps, DataOps, MLOps, and ModelOps. Master enterprise AI governance, CI/CT, and scalable platform engineering strategies. Continue reading on Medium »

Compare DevOps, DataOps, MLOps, and ModelOps. Master enterprise AI governance, CI/CT, and scalable platform engineering strategies. Continue reading on Medium »

Why strong data workflows, reusable features, and traceable lineage are the foundation of scalable ML systems Continue reading on Medium »

Choosing the AI model is no longer the hard part in 2026. Continue reading on Level Up Coding »

Most ML tutorials stop the second the model says “87% accuracy.” This one keeps going -through tracking, evaluation, testing, logging, and… Continue reading on

Let’s be brutally honest for a second. Continue reading on Medium »

How to slash your vector database billing, eliminate memory map crashes, and master scalar quantization. Continue reading on Medium »

Project Architecture (AWS Infrastructure Overview) Continue reading on Medium »

Author(s): FS Stance Originally published on Towards AI. Self-Hosting Airflow at Home: Automating Stock Price Data Collection One of the main goals of creating

MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning (ML), DevOps, and Data Engineering to develop… Continue reading on Medi

GPU scheduling, MLOps platforms, and platform engineering strategies for AI workloads on Kubernetes in 2026. Optimize utilization and reduce costs.

We’ve all heard "it works on my machine," but when it comes to AI-driven features, that phrase is a...

I have hit this a few times.A PR lands late in the day, the RAG eval runs in under a minute, green...