MLOps is the bridge between Data Science and Engineering. It's the practice of applying DevOps principles to machine learning. Without it, your model stays in a Jupyter notebook forever.
Why MLOps Matters
Classic problem: "Works on my machine!" But production is different:
- 📊 Data drifts over time
- 🔄 Models need retraining
- ⚠️ Predictions degrade
- 🚀 You need to deploy faster
- 📈 You need to scale reliably
Core MLOps Components
- Experiment Tracking: MLflow, Weights & Biases, Neptune
- Model Versioning: Git + DVC or Model Registry (MLflow, Hugging Face)
- Data Management: DVC, Delta Lake, or Great Expectations for quality
- CI/CD Pipelines: GitHub Actions, GitLab CI, Jenkins
- Model Serving: FastAPI, BentoML, KServe, Seldon
- Monitoring: Prometheus, ELK, or specialized ML monitoring
The ML Lifecycle
Think beyond a single train-deploy cycle:
- 1. Data Ingestion: Collect and validate data
- 2. Preparation: Clean, transform, split data
- 3. Training: Experiment, track, compare models
- 4. Validation: Test on held-out data, cross-validate
- 5. Deployment: Package and serve the model
- 6. Monitoring: Track performance, detect drift
- 7. Retraining: Automatically retrain when performance drops
Building Your First Pipeline
Start simple:
- Version your data with DVC
- Track experiments with MLflow
- Use GitHub Actions for CI/CD
- Serve with FastAPI
- Monitor with basic logging
Avoiding Common Mistakes
Don't do this:
- ❌ Manual train-deploy cycles
- ❌ No experiment tracking (how do you know which model is best?)
- ❌ Hard-coded paths and configs
- ❌ No data versioning (can't reproduce results)
- ❌ Ignoring data drift
- ❌ Serving without monitoring
Tools I Recommend
- Experiment Tracking: MLflow (free, self-hosted) or Weights & Biases (paid, better UX)
- Model Registry: MLflow Model Registry or Hugging Face Model Hub
- CI/CD: GitHub Actions (free with repo) or GitLab CI
- Serving: FastAPI + Uvicorn for REST APIs
- Monitoring: Custom Prometheus metrics + Grafana
The ROI of MLOps
Good MLOps returns:
- ⏱️ 10x faster iterations (weeks → days)
- 🔍 Full reproducibility (know exactly what you trained)
- 🚀 Confident deployments (automated testing)
- 📊 Data-driven decisions (experiment tracking)
- 🛡️ Safer production (automatic rollbacks, monitoring)
Start Today: Set up MLflow and GitHub Actions. These two will transform how you develop models.