The MLOps Cycle. Questo articolo non è disponibile.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.