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Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

By: By: Publication details: Noida: Pearson , 2020Description: 228ISBN:
  • 9789389588507
Subject(s): DDC classification:
  • 006.31 KEL
Summary: Machine learning in production is a crash course in data science and machine learning for learners who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualisation, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments. They always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work
List(s) this item appears in: New Arrivals for the Month of September - 2023 | New Arrivals for the Month of November - 2023
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
Reference Book Reference Book Alliance College of Engineering and Design CSE & IT 006.31 KEL (Browse shelf(Opens below)) 1 Not for loan E13262
Book Book Alliance College of Engineering and Design CSE & IT 006.31 KEL (Browse shelf(Opens below)) 5 Available E13266
Book Book Alliance College of Engineering and Design CSE & IT 006.31 KEL (Browse shelf(Opens below)) 2 Available E13263
Book Book Alliance College of Engineering and Design CSE & IT 006.31 KEL (Browse shelf(Opens below)) 3 Available E13264
Book Book Alliance College of Engineering and Design CSE & IT 006.31 KEL (Browse shelf(Opens below)) 4 Available E13265
Book Book Alliance College of Engineering and Design CSE & IT 006.31 KEL (Browse shelf(Opens below)) Available E12187
Total holds: 0

Machine learning in production is a crash course in data science and machine learning for learners who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualisation, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments. They always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work

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