Production ML: A Reality Check on MLOps Practices

Introduction

MLOps has become an industry buzzword with vendors promising automated ML pipelines. This reality check examines what actually works in production ML based on years of real-world experience.

What Works

Version Control for Everything

• Models: Track every version
• Data: Snapshot training data
• Configs: Version hyperparameters
• Pipelines: Code as infrastructure

Monitoring from Day One

# Essential monitoring
metrics = {
    'input_drift': detect_distribution_shift(input_data),
    'prediction_drift': track_prediction_distribution(predictions),
    'latency': measure_inference_time(model),
    'accuracy': compare_to_ground_truth(predictions, labels)
}

Simple Deployment Pipelines

Reality: Complex pipelines break

• Use standard containers
• CI/CD with tests
• Gradual rollouts

What Doesn't Work

Over-Automated Retraining

Problems:

• No understanding of failures
• Cascading issues
• Hidden technical debt

Better approach:

• Triggered retraining with approval gates
• Human review of significant changes
• Clear rollback procedures

Feature Store Complexity

Many teams over-invest:

• Build only what you need
• Start with simple storage
• Add complexity when proven necessary

One-Size-Fits-All Platforms

Different use cases need different approaches:

• Batch vs. real-time
• Small models vs. large models
• Experimental vs. production

Practical Guidelines

Start Simple

Week 1: Model in notebook
Week 2: Model as script with config
Week 3: Model as service with monitoring
Week 4: Model with basic pipeline

Build vs. Buy

Build when:

• Core competency
• Unique requirements
• Scale justifies investment

Buy when:

• Commoditized solution
• Limited ML eng capacity
• Time-to-market critical

Team Structure

What works:

• Embedded ML engineers in product teams
• Central platform team for infrastructure
• Clear ownership and on-call

What fails:

• Isolated ML team throwing models over the wall
• No production responsibility for data scientists
• Unclear ownership

Red Flags

Watch out for:

1. Months without production deployment
2. No monitoring on live models
3. Manual deployment steps
4. No reproducibility guarantees
5. Unclear ownership of models

Recommendations

1. Invest in fundamentals before fancy tools
2. Production exposure early in project lifecycle
3. On-call responsibility for ML engineers
4. Documentation and runbooks from start
5. Regular review of ML systems health

---

Get practical MLOps guidance in our production-focused courses.