BBBahati Brenda Kizitoingodfident-data.hashnode.dev·7h ago · 13 min readModel Drift vs Data Drift: What's Actually the Difference?Your model is making predictions, the API is responding, nothing appears to be broken. But something has changed. The transactions reaching your fraud detection model look different from the ones it w00
BBBahati Brenda Kizitoingodfident-data.hashnode.dev·1d ago · 19 min readI Finally Understand Data Drift: Your ML Model Didn't Break, The Data Changed.I used to think that once my model was trained on good data, the data problem was basically over. Then I learned about data drift. And suddenly I had a slightly uncomfortable question: What happens wh00
BBBahati Brenda Kizitoingodfident-data.hashnode.dev·2d ago · 15 min readYour ML Model Isn't Done When It Gets 95% AccuracyYour model scored 95%. Congratulations. Now comes the part nobody told you about. Because 95% accuracy might look amazing in a notebook and still leave you with a machine learning system that is slow,01A
BBBahati Brenda Kizitoingodfident-data.hashnode.dev·3d ago · 18 min readI Finally Understand MLOps — Here’s How I’d Explain It to a BeginnerI used to think MLOps was just DevOps with a machine learning model attached. And honestly? I wasn't completely wrong. I was just missing the part that makes machine learning… well, machine learning i01A
BBBahati Brenda Kizitoingodfident-data.hashnode.dev·Aug 17 · 8 min readMy Model Worked… Until I Actually Looked at the DataI thought I had a working model. The numbers looked clean. Accuracy sat comfortably high. The pipeline ran without errors. I even started thinking about how I would present the results. Then I actuall00
