Description
Machine Learning in Production is a practical guide that bridges the gap between developing machine learning models and deploying them in real-world production environments. Written by Andrew Kelleher and Adam Kelleher, this Pearson Education publication addresses the critical challenges that data scientists and engineers face when transitioning from experimentation to production systems.
The book covers essential topics including model deployment strategies, monitoring and maintenance of live models, handling data pipelines, and managing model performance over time. It provides readers with actionable insights and proven methodologies for building robust ML systems that can scale effectively. With a focus on practical implementation, the authors share real-world examples and lessons learned from deploying machine learning solutions across various industries and use cases.
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