MLOps Emerges - Making AI/ML Production-Ready
The initial excitement about machine learning had evolved into the hard work of making ML systems production-ready. The term "MLOps" captured the growing recognition that ML wasn't just about training models—it was about building reliable, scalable systems that could deliver business value consistently. Many organizations discovered that training a model was only 5% of the work. The real challenges were data pipeline reliability, model versioning, deployment automation, monitoring for drift, and maintaining performance over time. python # ML Pipeline with MLflow tracking import mlflow import mlflow.sklearn from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score def train_model(data_version, hyperparameters): with mlflow.start_run(): # Log parameters mlflow.log_...