R
Rishikesh Baidya
Author
September 1, 202513 min read
Development
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Deploying AI models is just the beginning. As Rishikesh Baidya, our CTO, learned building AI features for TalkDrill and ExamReady: operating AI reliably in production requires robust MLOps practices. Here's what you need to know.
87%
ML Projects Fail Production
3-6 mo
Model Degradation Timeline
40%
Time Spent on Operations
2x
Faster Iteration with MLOps
Why MLOps Matters
The Challenge
⚠️ AI ≠ Traditional Software: AI systems differ fundamentally from traditional software. Models degrade over time, data drift affects performance, retraining is ongoing, and monitoring is complex. Without MLOps, your AI will silently fail.
Model Degradation
Performance drops as real-world data diverges from training data
Data Drift
Input distributions change, affecting prediction accuracy
Continuous Retraining
Models need regular updates to stay accurate
Complex Monitoring
Traditional metrics don't capture ML-specific issues
MLOps Goals
- Reliable, reproducible model deployment
- Continuous improvement with data feedback
- Quality assurance for model outputs
- Operational efficiency at scale
- Compliance with AI regulations
Core Practices
1. Model Versioning
"If you can't reproduce your model training exactly, you can't debug problems, roll back safely, or explain predictions. Versioning isn't optional—it's foundational."
- Track everything:
- Model artifacts (weights, architecture)
- Training data (version, splits)
- Configuration (hyperparameters, environment)
- Metrics (training, validation, test)
Tools: MLflow, DVC, Weights & Biases, Neptune
2. Experiment Tracking
1
Log Hyperparameters
Every configuration setting that affects training outcomes.
2
Track Training Metrics
Loss curves, accuracy, precision, recall—whatever matters for your use case.
3
Store Artifacts
Model checkpoints, evaluation reports, sample predictions.
4
Enable Comparison
Compare experiments side-by-side to understand what works.
3. CI/CD for ML
Data Validation
Training
Evaluation
Registry
Deployment
- Automation triggers:
- Data changes (new training data)
- Code changes (model architecture, preprocessing)
- Scheduled retraining (weekly, monthly)
- Performance degradation (automatic alerts)
4. Model Serving
| Pattern | Best For | Considerations |
|---|---|---|
| Real-Time Inference | User-facing predictions | Low latency, high availability needed |
| Batch Prediction | Bulk scoring, reports | Cost-efficient, tolerates latency |
| Edge Deployment | Mobile, IoT devices | Model size constraints, offline support |
| Streaming Inference | Real-time data streams | Complex infrastructure, stateful processing |
Monitoring
Model Performance Monitoring
Prediction Accuracy
Track model accuracy against ground truth when available
Latency Percentiles
p50, p95, p99 inference times for SLA compliance
Throughput
Requests per second, capacity planning
Error Rates
Failed predictions, invalid inputs, exceptions
Data Drift Detection
Key Insight: Data drift is the silent killer of ML systems. Your model was trained on historical data, but real-world data constantly evolves. Detecting drift early is crucial.
- Types of drift:
- Feature drift: Input distributions change
- Label drift: Target variable distribution changes
- Concept drift: Relationship between inputs and outputs changes
- Response strategy:
- Automated alerts when drift exceeds thresholds
- Automatic retraining triggers for severe drift
- Manual investigation for unexplained drift
Data Management
Feature Store
Centralize feature engineering for consistency:
- Consistent features across training and serving
- Reusability across multiple models
- Point-in-time correctness for training
- Online/offline feature parity
💡 Our Stack: For AI automation projects, we use a combination of Feast for feature storage and custom pipelines for domain-specific feature engineering.
Governance
Model Registry
Model Inventory
Central catalog of all deployed models
Metadata
Training data, performance metrics, owner info
Lineage
Data sources, training pipelines, dependencies
Approval Status
Review gates, compliance checks, deployment status
Documentation Requirements
- Model cards describing purpose, limitations, and appropriate use
- Data documentation including sources, biases, and preprocessing
- Decision logs explaining key architectural choices
- Performance history over time
Architecture Patterns
Real-Time Inference Architecture
code
Request → API Gateway → Feature Store → Model Service → Response
↑
Feature Engineering (cached)Batch Prediction Architecture
code
Data Lake → ETL → Model → Predictions → Data Warehouse
↑ ↓
Schedule/Trigger Application AccessTools Ecosystem
| Category | Tools | Our Recommendation |
|---|---|---|
| Platforms | Kubeflow, SageMaker, Vertex AI | SageMaker for AWS projects |
| Tracking | MLflow, W&B, Neptune | MLflow for open-source |
| Feature Stores | Feast, Tecton, Hopsworks | Feast for flexibility |
| Monitoring | Evidently, Fiddler, WhyLabs | Evidently for drift detection |
Best Practices
1
Start Simple
Begin with basic monitoring and versioning. Add complexity as you understand your system's needs.
2
Automate Carefully
Automate what's well-understood. Keep humans in the loop for critical decisions. Use gradual rollouts.
3
Document Everything
Model decisions, data sources, performance baselines, and incident responses. Your future self will thank you.
✅ Our Approach: For AI projects like TalkDrill, we implemented MLOps from day one—resulting in 99.5% model availability and the ability to deploy model updates within hours, not weeks.
Related Resources
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