Design an ML monitoring system (drift, performance, outliers, and quality) for a fantasy sports app.

This question is about designing a monitoring system specifically for a Machine Learning model in the context of a fantasy sports app. The question lists some areas to cover i.e., drift, performance, outliers, and quality.

• Drift refers to the changes in the underlying patterns of the data over time.
• Performance indicates how well your model is doing its job.
• Outliers are unusual data points that are significantly different from other data, which could affect the model's performance.
• Quality refers to the overall effectiveness, efficiency, and accuracy of the model.

You need to provide a summary of how you would monitor each of these aspects in relation to the Machine Learning application in the fantasy sports app. You should consider the specific needs and features of such an app while formulating your answer. For instance, the app would need to handle fluctuating streams of real-time data, anticipate future events based on current data, and adapt its decisions and predictions over time.

Drift can be monitored by comparing the distribution of prediction inputs from the live model with the distribution of the data it was trained on. In our fantasy sports app, data like player stats could change according to ongoing events. We would need to design an alert mechanism when there's a significant variation, indicating a 'drift'.

Performance would primarily be monitored through key metrics of the models' effectiveness like precision, recall, F1 score, etc. Depending on the model's role, additional performance measures could be required. For example, if it's predicting game outcomes or player performances, we would compare actual results with the models' predictions over time.

Outliers can be detected via statistical methods or specific outlier detection algorithms. These could be high performing players, a surprising result of a match, etc. We could then decide how we want to handle these outliers - such as retraining the model to incorporate them or excluding them from the training data.

We could define Quality as our models' ability to generate actionable predictions that enhance user engagement and retention. This can be measured holistically through user feedback, usage analytics, and business KPIs such as app engagement, time spent on the app, etc.

With a drift detection system, we would focus on detecting concept drift. As tactics and strategies in sports can change over a season, our model needs to adapt to those situations. For this, we need to continuously monitor prediction errors and trigger re-training when these exceed a predefined threshold.

For performance monitoring, we'd use a dashboard to display metrics that measure how well our Machine Learning model is predicting future results. We'd look at both business-focused metrics (like conversion rates) and ML-focused metrics (like AUC-ROC, log-loss, RMSE), depending on our model’s goal.

Outliers in sports can drastically affect our models, such as an underdog team winning against a top-rated team. We need to use robust outlier detection methods to quickly identify these anomalies, analyze their impact and decide whether to treat them as a new trend or isolate them.

Quality would be monitored by ensuring that the app's content and recommendations are useful and engaging for the users. By measuring user-centric metrics like daily active users, churn, average session time, and action completion rates, we can keep checking whether our model is consistently providing quality predictions and insights.