Jan 14 · 3 min read · 📜 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...
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Jan 13 · 3 min read · 📜 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...
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Jan 12 · 3 min read · 📜 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...
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Jan 10 · 3 min read · 📜 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...
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