87% of ML projects never make it to production. The gap between a working notebook and a production system is vast.
The MLOps Lifecycle
- . **Data Management**: Version datasets, track lineage, ensure quality
- . **Experimentation**: Track experiments, compare results, reproduce findings
- . **Training Pipelines**: Automate training, hyperparameter tuning, validation
- . **Model Registry**: Version models, track metadata, manage approvals
- . **Deployment**: A/B testing, canary releases, rollback capabilities
- . **Monitoring**: Detect drift, track performance, trigger retraining
Key Tools
- **MLflow**: Experiment tracking and model registry
- **Kubeflow**: ML pipelines on Kubernetes
- **Weights & Biases**: Experiment tracking and visualization
- **Seldon/KServe**: Model serving at scale
Common Pitfalls
- Training-serving skew
- Lack of reproducibility
- No monitoring for drift
- Manual deployment processes
Invest in MLOps infrastructure early. It pays dividends as you scale.