Monitoring in MLOPS: Reflections on Module 5 of the MLOps Zoomcamp
In today’s fast‑paced ML landscape, deploying a model is only half the story. Continuous monitoring ensures your system stays healthy, accurate, and reliable long after go‑live. I’ve just wrapped up Module 5: Model Monitoring in the DataTalksClub MLOps Zoomcamp, and here’s what I learned—and how you can apply it to your own projects.
Why Model Monitoring Matters
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Drift Detection: Data distributions evolve. What trained your model yesterday may not reflect today’s reality.
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Quality Assurance: Spot issues like missing values or unexpected outliers before they impact end‑users.
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Reliability & Trust: Stakeholders need confidence that your predictions remain valid and service levels remain high.
Drift Detection: Data distributions evolve. What trained your model yesterday may not reflect today’s reality.
Quality Assurance: Spot issues like missing values or unexpected outliers before they impact end‑users.
Reliability & Trust: Stakeholders need confidence that your predictions remain valid and service levels remain high.
Core Components of the Monitoring Stack
1. Docker Compose Services
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PostgreSQL for storing time‑series metrics
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Adminer for lightweight database management
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Grafana for rich, interactive dashboards
Spinning these up with a singledocker-compose up
command made the setup a breeze.
2. Evidently for Data & Concept Drift
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Compute drift metrics (e.g., feature‑level drift on model predictions)
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Monitor data quality (missing values, data ranges, quantiles)
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Generate ad‑hoc test suites and reports to debug anomalies on demand
3. Prefect for Automation
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Automate batch metric collection at regular intervals
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Orchestrate data loading, metric computation, and database writes in a managed flow
(In this module, Prefect was used for demonstration—keep in mind it’s optional in some course editions.)
4. Grafana Dashboards
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Pre‑configured panels visualize your metrics over time:
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Missing‑Value Counts
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Data Drift Scores
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Quantile Trends (e.g., median fare amount)
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Dashboards are exported as JSON and saved under
05-monitoring/dashboards/
for version control and easy reloads.
1. Prepare Data
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Train a baseline model and generate a reference dataset.
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Simulate “current” batches (e.g., sliding daily or monthly windows).
2. Compute Metrics
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Run a Python script to calculate Evidently metrics in a loop (every 10 seconds for demo, representing daily batches).
- Insert results into PostgreSQL.
3. Visualize & Alert
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Open Grafana at
localhost:3000
(defaultadmin/admin
credentials). -
Browse the “Home → New Dashboard” folder for the pre‑built monitoring dashboard.
- Review panels for drift, data quality, and test failures.
4. Debug on Demand
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Use the ad‑hoc
debugging_nyc_taxi_data.ipynb
notebook to run Evidently TestSuites. -
Drill into unexpected metric spikes or failing tests for root‑cause analysis.
Modular Architecture: Decouple metric computation (Evidently) from storage (PostgreSQL) and visualization (Grafana).
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Automation Is Crucial: Even a simple Prefect flow ensures metrics are fresh and consistent.
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Version‑Controlled Dashboards: Saving dashboard JSON alongside code makes reproducibility and collaboration seamless.
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Proactive Debugging: Integrating TestSuites lets you catch issues before they cascade into production incidents.
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