Case Study 1: Setting Up an MLOps Pipeline

Overview

Company: FinPredict, a fintech company serving 50,000 business customers with credit risk scoring. Challenge: The data science team of 8 engineers manages 12 models in production with no standardized pipeline. Model updates take 3-6 weeks from development to deployment, and two incidents in the past year involved deploying models trained on incorrect data versions. Goal: Build an end-to-end MLOps pipeline that reduces deployment time to under 2 days, eliminates data versioning errors, and provides comprehensive monitoring.

Problem Analysis

A thorough audit revealed the following operational issues:

1. No experiment tracking: Data scientists used spreadsheets and Jupyter notebook cell outputs to track experiments.
2. Manual deployment: Models were deployed by copying pickle files to a shared directory and restarting the serving application.
3. No data versioning: Training data was stored on local machines. There was no record of which data version produced which model.
4. No monitoring: The team discovered a model degradation only after customer complaints accumulated over 3 weeks.
5. No testing: No automated tests for data quality, model quality, or serving correctness.

Cost of the Current Approach

Architecture Design

Tool Selection

Pipeline Architecture

Code Change (Git) --> GitHub Actions CI
  |
  |--> Data Validation (Great Expectations)
  |--> Unit Tests (pytest)
  |--> Training Pipeline (DVC + W&B)
  |      |--> Data loading (DVC-versioned)
  |      |--> Feature engineering
  |      |--> Model training (W&B tracking)
  |      |--> Evaluation (quality gates)
  |
  |--> Model Registry (W&B)
  |      |--> Staging --> Production promotion
  |
  |--> Deployment (Docker + Kubernetes)
  |      |--> Canary rollout (1% -> 5% -> 25% -> 100%)
  |
  |--> Monitoring (Prometheus + Grafana)
         |--> Feature drift detection
         |--> Prediction distribution monitoring
         |--> Latency and error rate tracking
         |--> Automated retraining triggers

Implementation

Phase 1: Experiment Tracking (Week 1-2)

The team standardized all training code to use W&B logging:

• Every training run logs hyperparameters, metrics, data checksums, and git commit hash
• Confusion matrices and ROC curves are logged as artifacts
• Model checkpoints are saved with full provenance metadata

Result: Within 2 weeks, the team identified 3 previously unknown hyperparameter configurations that outperformed the current production model.

Phase 2: Data Versioning (Week 3-4)

DVC was integrated to version all training datasets:

• Raw data snapshots are stored in S3 with DVC tracking
• Feature engineering scripts are part of the DVC pipeline
• Every model links to its exact data version via DVC commit hash

Result: Data versioning errors were eliminated completely.

Phase 3: CI/CD Pipeline (Week 5-7)

GitHub Actions workflows were created for:

• On PR: Run data validation, unit tests, and lint checks
• On merge to main: Run full training pipeline, evaluate against quality gates
• On model promotion: Build Docker image, run integration tests, deploy canary

Quality gates required: - AUC-ROC >= 0.82 (current production baseline) - Calibration error <= 0.05 - Max prediction latency <= 50ms (p99) - No fairness regression (demographic parity within 5%)

Phase 4: Monitoring (Week 8-10)

Comprehensive monitoring was deployed:

• Feature drift: PSI computed daily for all 45 input features
• Prediction drift: Distribution of predicted probabilities tracked hourly
• Performance: Daily accuracy estimation using delayed ground truth labels
• System: Latency, throughput, error rates, GPU utilization

Automated alerts trigger when: - Any feature PSI > 0.25 for 3 consecutive days - Prediction distribution KL divergence > 0.1 - Estimated accuracy drops below 0.80 - p99 latency exceeds 100ms

Results

Deployment Metrics

Engineering Efficiency

Business Impact

• Model degradation incidents reduced from 4/year to 1/year
• The faster experimentation cycle led to a 2.3% improvement in model AUC-ROC within the first quarter
• Estimated annual savings: $350K in engineering time + $100K in prevented revenue loss

Key Lessons

1. Start with experiment tracking. It provides immediate value with minimal infrastructure change and builds team buy-in for the broader MLOps initiative.

2. Quality gates are the most impactful CI/CD addition. Automated quality checks prevented 3 would-be regressions in the first month alone.

3. Monitoring is not optional for production ML. The 125x improvement in detection time was the single most valuable outcome. Silent model degradation is far more costly than the monitoring infrastructure.

4. Incremental adoption works better than big-bang. The phased rollout over 10 weeks allowed the team to learn and adjust. Attempting to deploy everything at once would have overwhelmed the team.

5. Data versioning pays for itself immediately. The elimination of data version errors and the ability to reproduce any past training run justified the DVC investment within the first month.

Code Reference

The complete implementation is available in code/case-study-code.py.