Managing Machine Learning Projects: From design to deployment by Simon Thompson.
Simon holds a Phd in Philosophy from the University of Oxford UK. His is the former Director of AI research at BT Labs. Today he serves as the Head of Data Science at GFT Technologies.
Simon is revealing that managing ML projects to production can seem like navigating uncharted waters.
By revealing the challenges of accounting for large data resources to tracking multiple models, machine learning has very different requirements when compared to building traditional software applications. Simon does acknowledge this challenge and asks readers to execute this on a more even scale.
Managing Machine Learning Projects is an end-to-end guide for delivering ML applications on time and under budget. Simon is revealing the tools, approaches, and processes designed to handle the challenges of machine learning project management. To help readers Simon is deploying a full case study.
ML projects: more than meets the eye
This book places strong consideration to data privacy, and ethical considerations which addresses community impacts of machine learning projects which contain solutions to bias and other issues. If fact, his role of risk management focuses on identifying and mitigating risks specific to projects.
Please consider the following ML books to further your understanding: Machine Learning: The New AI by Ethem Alpaydin, Grokking Machine Learning by Luis Serrano and Privacy-Preserving Machine Learning by J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera.
Of course Simon is also making readers aware of model lifecycle management. To be successful one must be able to handle multiple models throughout a project’s lifecycle when working with clients and stakeholders. He is also showing the necessary guidance on how to set up the compute infrastructure including data management and model evaluations.
In conclusion, Managing Machine Learning Projects is a very practical guide targeting professionals who are now navigating the complexities of managing machine learning projects. From defining project requirements, planning, execution, to deployment, the book is designed for readers without technical backgrounds, making it accessible to a wider audience interested in managing Machine Learning Projects.