uplatz.hashnode.dev🏷 MLOps Explained – Monitoring Models in Production📜 Why Monitoring Is Critical in Production ML Unlike traditional software, machine learning models change behaviour over time. Even when code stays the same, models can fail due to: Changing data patternsShifts in user behaviourSeasonality and trend...Jan 14·3 min read
uplatz.hashnode.dev🏷 MLOps Explained – Model Deployment Patterns: Batch, Real-Time & Edge📜 Why Model Deployment Is Not One-Size-Fits-All Deploying a machine learning model is not just about making predictions available. Deployment decisions affect: System architectureUser experienceOperational costModel performance and reliability Diffe...Jan 13·3 min read
uplatz.hashnode.dev🏷 MLOps Explained – Model Training, Validation & CI/CD📜 Why Training and Deployment Can’t Be Manual In early ML projects, training and deployment are often manual: Run a notebookSave a model fileUpload it to production This approach fails at scale. Problems include: Inconsistent resultsHuman errorNo qu...Jan 12·3 min read
uplatz.hashnode.dev🏷 MLOps Explained – Data Versioning & Experiment Tracking📜 Why Data and Experiments Must Be Tracked In machine learning, data changes everything. A small change in data can lead to: Different model behaviourDifferent performance metricsDifferent business outcomes Without proper tracking, teams cannot answ...Jan 10·3 min read
uplatz.hashnode.dev🏷 MLOps Explained – What Is MLOps and Why It Matters📜 What Is MLOps? MLOps (Machine Learning Operations) is a set of practices that combines: Machine LearningSoftware EngineeringDevOps Its goal is to reliably deploy, monitor, and maintain machine learning models in production. While data science focu...Jan 9·3 min read
