The Practical Guide to AI/ML in Production

Everyone is excited about AI and ML—and rightfully so. But there's a massive gap between a model that works in a Jupyter notebook and one that reliably serves millions of users in production. ## The Reality of ML in Production Here's what they don't tell you in ML courses: - 80% of the work is data engineering, not model development - Models degrade over time as real-world data drifts from training data - Latency requirements often conflict with model complexity - Explainability is not optional in regulated industries ## Our ML Platform Architecture After several painful production incidents, we built a platform with these components: ### 1. Feature Store A centralized repository for feature computation and storage. This solved our biggest problem: different teams computing the same features differently. ### 2. Model Registry Version control for models. Every model artifact is tagged with: - Training data snapshot - Hyperparameters - Evaluation metrics - Champion/challenger status ### 3. Serving Infrastructure We use a tiered serving approach: - Real-time (< 10ms): Pre-computed features, simple models - Near real-time (< 100ms): Streaming features, complex models - Batch: Scheduled predictions for non-latency-sensitive use cases ## Monitoring Is Not Optional ML monitoring is fundamentally different from traditional software monitoring. You need to track: - Data drift: Has the input distribution changed? - Concept drift: Has the relationship between inputs and outputs changed? - Model performance: Are predictions still accurate? - Business metrics: Is the model achieving its intended business outcome? ## Conclusion ML in production is hard, but it's becoming a competitive necessity. The organizations that invest in proper ML infrastructure today will have a significant advantage in the AI-first future.