01What MLOps actually is
MLOps is the discipline of operating machine learning in production. It is DevOps plus data and model lifecycle — version control for code, data, and models; CI/CD; monitoring; and a retraining loop.
Machine Learning · September 11, 2025
MLOps from Scratch: Deploying Your First ML Model to Production
A no-fluff walkthrough for going from a notebook model to a versioned, monitored, retrainable production service in two weeks.
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Binate AI
September 11, 2025
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02The minimum viable stack
- Source control: Git for code + DVC or LakeFS for data
- Experiment tracker: MLflow, W&B, or Comet
- Model registry: MLflow registry or SageMaker model registry
- CI: GitHub Actions or GitLab CI
- Serving: BentoML, Triton, or a simple FastAPI service behind a load balancer
- Monitoring: Evidently or Arize for data/model drift
03Step-by-step deployment
- 1
Containerize training
Move your notebook into a Python module. Pin dependencies in a lockfile. Build a Docker image with a deterministic environment.
- 2
Track the experiment
Log parameters, metrics, and the trained artifact to your tracker. You will want this in three months when someone asks "which model is in prod?".
- 3
Register the model
Promote the chosen experiment to a model registry with a version tag. The registry is the source of truth.
- 4
Wrap it in a serving API
A 30-line FastAPI service does the job for V1. Standardize input schema; return the prediction plus the model version.
- 5
Deploy with CI/CD
On every promoted model version, build and deploy. Use canary rollout (5% traffic) before full cutover.
- 6
Monitor and retrain
Watch input data drift and output performance. Trigger retraining on drift, scheduled cadence, or labeled feedback.
04A FastAPI serving example
serve.py
from fastapi import FastAPI
import mlflow, joblib, os
MODEL_VERSION = os.environ["MODEL_VERSION"]
model = mlflow.pyfunc.load_model(f"models:/churn/{MODEL_VERSION}")
app = FastAPI()
@app.post("/predict")
def predict(features: dict):
score = float(model.predict([features])[0])
return {"score": score, "model_version": MODEL_VERSION}05Monitoring — the part teams skip
Models silently degrade. You need three monitors: input data distribution (drift), prediction distribution (concept drift), and ground truth when it arrives (live accuracy).
06When to retrain
Quick Quiz
Your input data shifted, accuracy dropped 4%, and you have new labeled data. What is the right move?
07
Stand up an MLOps platform in 8 weeks
Binate AI sets up MLflow, registry, CI/CD, and monitoring tailored to your stack.
Get an MLOps auditThe takeaway
MLOps is not exotic — it is software engineering for ML. Track experiments, register models, monitor outputs, retrain on drift.
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