MLOps IN PRACTICE: Automation and Scalability for AI Models
MLOps IN PRACTICE is an essential guide for professionals looking to take Machine Learning models from experimentation to production with efficiency, scalability, and continuous automation. In this book, you will learn how to implement robust pipelines, monitor AI models in real time, and apply the best MLOps practices to ensure performance, reliability, and governance in Artificial Intelligence projects.
Written by Diego Rodrigues, a best-selling author with over 180 titles published in six languages, this book combines theory and practice, offering a modern and applied approach to the current MLOps landscape. Throughout the chapters, you will explore essential frameworks and tools such as Docker, Kubernetes, CI/CD for Machine Learning, MLflow, TensorFlow Extended (TFX), FastAPI, and more.
You will learn how to:
Automate and scale Machine Learning pipelines with advanced versioning and monitoring techniques.
Implement CI/CD for AI models, ensuring continuous training, deployment, and retraining.
Manage models in production by applying observability, traceability, and bias mitigation practices.
Utilize leading industry tools such as Kubeflow, MLflow, Airflow, and TFX to orchestrate ML workflows.
Enhance AI governance and security, ensuring compliance with regulations and international standards.
With practical examples, case studies, and established frameworks, MasterTech: MLOps in Practice is not just a technical manual-it is an indispensable resource for data scientists, ML engineers, software architects, and technology leaders looking to implement MLOps strategically and at scale.
Get ready to revolutionize the way you manage AI models in production and master the most advanced MLOps techniques in 2025
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