Custom ML Metrics with Prometheus
Go Beyond Latency: Export Predictions per Class, Confidence Scores, Feature Statistics, and Model Version So Your Dashboard Shows What the Model Is Doing.
100 Days of MLOps44
This roadmap takes you from zero MLOps knowledge to a production-ready ML engineer capable of building, deploying, monitoring, and automating end-to-end machine learning pipelines. You will master tools like MLflow, Docker, Kubernetes, Kubeflow, and cloud ML platforms while applying best practices in CI/CD, model governance, and observability. By Day 100, you will be able to design and operate scalable, reliable ML and LLM systems in real-world environments.
Check out the latest posts
Prometheus and Grafana for ML System Monitoring
Instrument Your FastAPI Model Server, Scrape It With Prometheus, and Build a Grafana Dashboard for Request Rate and Latency in One Docker Compose Stack.
Week 6 Review: Data Quality and Monitoring Foundations
Wire Validation, Drift Checks, and a Monitoring Plan Into One Pipeline That Refuses to Train on Bad Data.
Introduction to Model Monitoring Concepts
Know Exactly What to Watch in Production: Data Quality, Drift, Model Performance, and System Health, Each With a Threshold and an Owner.
Data Drift Detection Fundamentals
Catch Production Data Drifting Away From Your Training Data With KS, PSI, and Chi-Squared Tests Before Accuracy Quietly Drops.
Data Validation with Great Expectations
Write Your Data Assumptions as Tests, Run Them on Every Batch, and Stop the Pipeline When They Break.