MLOps

The “MLOps” category is dedicated to exploring the practices, tools, and frameworks for efficiently developing, deploying, and maintaining machine learning systems in production environments. Here you’ll find resources that cover the end-to-end lifecycle of ML projects, from data ingestion and model training to deployment, monitoring, and continuous improvement. The materials dive into popular MLOps platforms like MLflow, Kubeflow, and TensorFlow Extended, and guide you through setting up scalable and reproducible ML pipelines using containerization, orchestration, and version control. You’ll learn how to automate model testing, validation, and deployment using CI/CD workflows, and how to monitor model performance, detect drift, and trigger retraining using tools like Prometheus and Grafana. The category also covers important topics like model serving, A/B testing, and infrastructure scaling, along with best practices for collaboration, documentation, and governance in ML projects. Whether you’re a data scientist looking to productionize your models or an ML engineer responsible for building and maintaining robust ML systems, these resources will provide you with the knowledge and skills to streamline your workflows, ensure the reliability and scalability of your ML deployments, and drive business value with machine learning.