MLOps: Bridging Machine Learning and Operations
Machine Learning has moved out of the lab and into production. But here is the hard truth: a model in a Jupyter notebook is not a product. To get value from AI, you need MLOps. MLOps is the discipline of applying DevOps practices to Machine Learning workflows.
The Model is Only Part of the System
Google published a famous paper showing that the ML code is only a tiny fraction of a real-world ML system. The rest is data collection, feature extraction, verification, resource management, analysis tools, and serving infrastructure. As an engineer, you need to build the *system*, not just the model.
Data Versioning is Critical
In traditional software, code versioning is enough. In ML, you need to version the code, the data, and the model parameters. If you retrain a model and it performs worse, you need to know exactly what data was used. Tools like DVC (Data Version Control) allow you to track data changes just like you track code changes in Git.
Continuous Training (CT)
Models decay. The world changes, and the data patterns that your model learned last month might not apply today. This is called "concept drift." You need a pipeline that can automatically retrain your model when performance drops or when new data is available. This is the "Continuous Training" aspect of MLOps.
Model Monitoring and Observability
Monitoring an ML model is different from monitoring a web server. You don't just care about latency and error rates. You care about prediction quality. Is the model's accuracy dropping? Is the input data distribution shifting (data drift)? You need specialized monitoring to catch these issues early.
The Feature Store Pattern
One of the biggest pain points in ML is feature engineering. Data scientists create features for training, and engineers have to recreate them for serving. This leads to inconsistencies. A Feature Store solves this by providing a single source of truth for features, used for both training and serving.
Reproducibility is Non-Negotiable
If you cannot reproduce a model, you cannot trust it. You must be able to go back to any version of a model and know exactly how it was built. This requires strict tracking of experiments, hyperparameters, and datasets. Without this, you are just guessing.