Machine Learning Systems in Production by Steve Millán (.ePUB)+
File Size: 10 MB
Machine Learning Systems in Production: Practical Strategies for Production-Ready ML, Automation, and Continuous Improvement by Steve Millán
Requirements: .ePUB, .PDF, .MOBI reader, 10 MB
Overview: What separates organizations that successfully deploy AI from those that don’t isn’t access to better algorithms — it’s the discipline to build systems that actually work in the real world. Machine Learning for Intelligent Automation is the definitive practitioner’s guide to designing, deploying, and sustaining ML-powered systems that don’t just predict — they decide, act, and improve. Written by systems architect and ML engineer Steve Millan, this book bridges the persistent gap between data science theory and production engineering reality. From classical algorithms to Deep Learning, from NLP pipelines to computer vision at industrial scale, from notebook prototypes to cloud-native infrastructure that handles millions of requests — this book covers the full stack. You’ll learn how to frame ML problems correctly, engineer data that actually teaches machines what you need them to learn, choose the right algorithm for the task at hand, and build the operational infrastructure that keeps models performing long after launch day. The tooling convergence is also real. Languages like Python have always bridged the worlds of Data Science and software engineering. Tools like Docker, Kubernetes, and Terraform are used as much for ML infrastructure as for traditional software infrastructure. MLflow and DVC are building on the foundations of Git and CI/CD systems. This convergence is good for the field: it reduces the friction between ML development and engineering, enables better collaboration between data scientists and software engineers, and accelerates the adoption of production engineering best practices in ML teams.
Genre: Non-Fiction > Tech & Devices
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