Mlops In Practice
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A Comprehensive Guide to Machine Learning Operations (MLOps)
Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries, revolutionizing how businesses make decisions, automate processes, and provide innovative products and services. Yet, the successful implementation of AI and ML goes beyond developing sophisticated models. It requires the seamless integration of these models into operational workflows, ensuring their reliability, scalability, security, and ethical compliance. This integration is the heart of Machine Learning Operations or MLOps. This comprehensive guide is your passport to understanding the intricate world of MLOps. Whether you are an aspiring data scientist, a seasoned machine learning engineer, an operations professional, or a business leader, this guide is designed to equip you with the knowledge and insights needed to navigate the complexities of MLOps effectively.
MLflow in Practice
"MLflow in Practice" "MLflow in Practice" is a comprehensive guide for data scientists, ML engineers, and enterprise practitioners seeking to harness the full power of MLflow in modern MLOps workflows. The book opens with a thorough exploration of MLflow’s core components—including Experiment Tracking, Projects, Models, and Model Registry—demystifying its architecture, deployment patterns, and seamless integration with leading platforms like Databricks, AzureML, Kubeflow, and Airflow. Readers gain valuable insights into positioning MLflow within the broader MLOps ecosystem, choosing between open source and enterprise offerings, and implementing robust security and governance practices from the outset. Delving deep into practical implementation, the book provides actionable best practices for managing experiments, logging and visualizing runs, packaging reproducible ML projects, and orchestrating scalable deployment pipelines. Advanced chapters address complex scenarios such as distributed experimentation, hybrid and multi-cloud deployments, model lifecycle management, automated retraining, and CI/CD integration. Coverage extends to securing sensitive data, ensuring compliance with industry regulations, and developing enterprise-ready ML systems with full traceability, auditability, and disaster recovery. Enriched with real-world case studies and forward-looking insights, "MLflow in Practice" showcases MLflow’s transformative role across diverse domains—from regulated enterprise environments and academic research to edge IoT and AI startups. Readers will not only learn how to deploy, monitor, and optimize ML models in production, but also stay ahead of emerging trends in generative AI, open standards, and collaborative experimentation. Whether you are modernizing machine learning operations or scaling ML workflows globally, this book equips you with the strategies, patterns, and technical know-how to maximize impact with MLflow.
Software Engineering Research and Practice and e-Learning, e-Business, Enterprise Information Systems, and e-Government
This book constitutes the proceedings of the 22nd International Conference on Software Engineering Research and Practice, SERP 2024, and the 23rd International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government, EEE 2024, held as part of the 2024 World Congress in Computer Science, Computer Engineering and Applied Computing, in Las Vegas, USA, during July 22 to July 25, 2024. For SERP 2024, 52 submissions have been received and 9 papers have been accepted for publication in these proceedings; the 12 papers included from EEE 2024 have been carefully reviewed and selected from 55 submissions. They have been organized in topical sections as follows: software engineering research and practice; e-learning, e-business, enterprise information systems and e-government.