IT Career Guides & Free Resources
Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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MLflow for Data Scientists: Tracking Experiments
Data scientists usually meet MLflow inside a notebook, where the useful habits are simple: log a baseline, group a sweep under one parent run, let autologging capture parameters, and compare everything as a DataFrame. This walkthrough runs that workflow on the UCI Wine Quality data with MLflow 3.16.
A/B Testing for Data Scientists: A Practical Guide
A/B testing from design to decision: one primary metric, calculating the sample size before launch, analysing conversion with a two-proportion test and a confidence interval, checking for sample ratio mismatch, and avoiding the traps (peeking, many metrics, novelty) that produce confident wrong answers.