AlgoInsight

Q&A

What is MLOps and deploying AI in production?

By AlgoInsight Team2026-06-18devopsmachine-learning

Overview

Building a model that works in a notebook is only 10-20% of the work. The remaining 80% is getting it deployed reliably, keeping it accurate over time, and retraining when the world changes. MLOps is the engineering discipline for all of that.

graph TD
    A[Data collection & versioning] --> B[Experiment tracking & model training]
    B --> C[Model evaluation & validation]
    C --> D[Model registry & versioning]
    D --> E[Model serving & CI/CD deployment]
    E --> F[Monitoring: drift, latency, errors]
    F --> G{Performance degraded?}
    G -->|yes| A
    G -->|no| F
    style E fill:#1e1b4b,color:#fff

The MLOps Stack

1. Data Versioning

Track changes to datasets like code changes. Tools: DVC (Data Version Control), Delta Lake, LakeFS. Enables reproducible training runs.

2. Experiment Tracking

Log hyperparameters, metrics, and artifacts for every training run. Tools: MLflow, Weights & Biases (W&B), Neptune. Answers: "which configuration produced that good result?"

import mlflow
mlflow.log_param("learning_rate", 0.001)
mlflow.log_metric("val_accuracy", 0.93)
mlflow.log_artifact("model.pkl")

3. Model Registry

A versioned catalog of trained models with metadata (metrics, lineage, stage: staging/production). MLflow Model Registry, W&B Registry, Vertex AI Model Registry.

4. Model Serving

Expose the model as an API endpoint:

# FastAPI inference endpoint
from fastapi import FastAPI
import torch

app = FastAPI()
model = torch.load("model.pt")

@app.post("/predict")
def predict(data: InputData):
    result = model(preprocess(data))
    return {"prediction": result.item()}

Tools: FastAPI, BentoML, TorchServe, Triton Inference Server (NVIDIA, batching + GPU), SageMaker, Vertex AI, KServe (Kubernetes).

5. CI/CD for ML

Automated pipelines that: validate data quality → retrain model → run evaluation tests → deploy only if metrics pass thresholds. Tools: GitHub Actions, Kubeflow Pipelines, Metaflow, ZenML.

6. Feature Stores

A centralized repository for computed features - ensures training and serving use identical feature computation (preventing training-serving skew). Tools: Feast, Tecton, Hopsworks.

Monitoring in Production

Data Drift

The statistical distribution of input data changes over time (users change behaviour, world events). Detect with: KS test, Jensen-Shannon divergence, PSI (Population Stability Index). Tools: Evidently AI, WhyLabs, Arize.

Concept Drift

The relationship between inputs and outputs changes (a model trained on pre-pandemic data fails post-pandemic). Requires model retraining.

Model Performance Monitoring

Track latency (P50, P95, P99), error rates, prediction distributions, and - when labels arrive - actual accuracy. Set alerts for degradation.

Logging and Observability

Log every prediction with input features for debugging and audit. For LLMs: log prompts, completions, latency, token counts, and any safety flags.

LLMOps - MLOps for LLMs

LLM-specific concerns:

Tools: LangSmith (LangChain), Arize Phoenix, Helicone, Braintrust.

Why This Matters in Interviews

MLOps questions appear in ML engineering and senior DS interviews. Cover: the full pipeline (data → experiment → registry → serving → monitoring), training-serving skew (the biggest silent killer), data drift vs concept drift, and the LLMOps extensions (prompt versioning, LLM evaluation, cost monitoring). The framing that "deploying is the hard part - models fail silently in production" shows engineering maturity.

Common Pitfalls

References

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