📰 Medium · Machine Learning

MLOps, short for machine learning operations, is a set of practice employed to make machine learning model development deployable, usable… Continue reading on M

PSI + KS testleri, FastAPI, Prometheus, MLflow ve auto-retrain ile uçtan uca mini MLOps mimarisi Continue reading on Medium »

Training a machine-learning model is only the beginning. The real challenge is getting it into the real world, keeping it reliable, and… Continue reading on Med

AI coding agents are the first contributors that read your entire repository and run shell commands non-interactively on day one. But… Continue reading on Mediu

Data pipelines have always been dumb by design — do exactly this, in exactly this order, and scream if anything moves. In 2026, teams are… Continue reading on M

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

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 »

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 »

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

The Business Problem Continue reading on Medium »

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

I ran Airflow at home to pull live stock data. Here’s how I did it and what I learned. Continue reading on Analytics Vidhya »

What AWS, Azure, and GCP each get right for MLOps, what they cost, and why your existing stack decides more than any feature list Continue reading on Medium »

A comprehensive, battle-tested guide to implementing CI/CD pipelines for Machine Learning projects — and why they’re fundamentally… Continue reading on Medium »

How an MLOps team used ephemeral Docker environments to safely test a car price prediction pipeline — stage by stage — before touching… Continue reading on Medi

Part 2 of an MLOps End-to-End series — 60 models, fully automated, one Airflow DAG Continue reading on Medium »

As part of my KodeKloud MLOps learning journey, Day 2 focused on setting up and troubleshooting a JupyterLab server configuration for a… Continue reading on Med

They sound alike. In production, they solve fundamentally different problems. Here’s exactly how they diverge. Continue reading on Medium »

Training a machine learning model is, relatively speaking, the easy part. What comes next — deploying it to production, maintaining it… Continue reading on Medi

Well… here’s a few answers Continue reading on Medium »

Google says ML code is 5% of a production ML system. Here’s what the other 95% costs you when you ignore it. Continue reading on Stackademic »

A complete MLOps walkthrough: Docker multi-stage builds, AWS ECS Fargate, ECR image management, GitHub Actions CI/CD with five automatic… Continue reading on Me