Udaya Veeramreddygari

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_...

Spring Boot 2.2 - Performance and Production Readiness Spring Boot 2.2 wasn't just an incremental update—it was a statement about production readiness and performance optimization. As organizations moved beyond proof-of-concept microservices to production-scale distributed systems, the framework evolved to meet these new demands. The introduction of lazy initialization was a game-changer for startup time and memory consumption. For organizations running hundreds of microservices, even small improvements in resource usage translated to significant cost savings. java @SpringBootApplication public class OrderServiceApplication {         public static void main(String[] args) {         System.setProperty("spring.main.lazy-initialization", "true");         SpringApplication.run(OrderServiceApplication.class, args);     } }   // Or configure selective...