The MLOps Cycle

Transform your machine learning projects into production-grade systems with The MLOps Cycle, your hands-on guide to mastering the complete ML lifecycle-from raw data ingestion to automated monitoring at scale.

Designed for data scientists, ML engineers, and DevOps practitioners, this book dives deep into the real-world tools and practices that power modern AI deployment. Learn how to streamline and automate every stage of your ML workflow using industry-standard technologies like Kubeflow, Apache Airflow, and Prometheus.

Through practical examples and project-driven chapters, you'll:

• Design robust data pipelines that handle preprocessing, validation, and feature engineering.

• Orchestrate end-to-end ML workflows with Airflow DAGs and Kubeflow Pipelines.

• Package, deploy, and version your models in cloud-native environments.

• Implement CI/CD pipelines tailored for ML with reproducibility and scalability in mind.

• Monitor model drift, performance, and system health using Prometheus and Grafana.

• Align machine learning operations with MLOps best practices across teams and lifecycles.

Whether you're building your first ML system or scaling enterprise-grade models in production, The MLOps Cycle offers the clarity, architecture, and automation mindset needed to succeed.