You have spent the last 29 lessons learning the individual disciplines that constitute modern MLOps — data pipelines, experiment tracking, model registries, REST APIs, containerisation, explainability, and drift monitoring. This capstone project knits all of those skills into a single, deployable artefact: a cricket player performance prediction system built on real IPL match patterns. By the end you will have a portfolio piece that demonstrates not just modelling ability, but the full engineering discipline required to take a model from a Jupyter notebook to a production container with observability baked in.
Analogy🏏Cricket
🏏 Think of it like cricket: Evidently AI is the IPL's official analytics platform — rather than each franchise building their own stats system, they use a shared platform that automatically computes every standardized metric: batting averages, economy rates, strike rates, net run rates. When Virat Kohli's performance drifts from his baseline, the platform highlights it automatically with charts. Evidently does the same for ML models: instead of each team coding their own drift detectors, they use Evidently's pre-built metrics and get standardized, comparable reports automatically. The standardization is the strategic point, not a convenience: because every franchise reads the same metric definitions, a drift score of 0.3 means the same thing in every dashboard, reports can be compared across teams and seasons, and a new analyst is productive on day one. Hand-rolled monitoring scripts fail exactly here — every team's 'drift check' quietly means something different, and nobody can audit whose alarm was right.