Machine Learning Infrastructure and Best Practices for Software Engineers : Take your machine learning software from a prototype to a fully fledged software system

Machine Learning Infrastructure and Best Practices for Software Engineers : Take your machine learning software from a prototype to a fully fledged software system

Αποθηκεύτηκε σε:

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας:	Staron, Miroslaw. (Συγγραφέας)
Μορφή:	E-Book
Γλώσσα:	Anglais
Έκδοση:	Birmingham : Packt Publishing.
Autres localisations:	Voir dans le Sudoc
Διαθέσιμο Online:	Accès à l'E-book

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Machine Learning Engineering with Python : Manage the lifecycle of machine learning models using MLOps with practical examples
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Perspectives on Data Science for Software Engineering
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Security-Driven Software Development : Learn to analyze and mitigate risks in your software projects
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Practical Machine Learning with R : Define, build, and evaluate machine learning models for real-world applications
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Ghidra Software Reverse Engineering for Beginners : Analyze, identify, and avoid malicious code and potential threats in your networks and systems
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Software Quality Assurance : In Large Scale and Complex Software-intensive Systems
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The essence of software
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Understanding Software
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Machine Learning Security with Azure : Best practices for assessing, securing, and monitoring Azure Machine Learning workloads
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Machine Learning Automation with TPOT : Build, validate, and deploy fully automated machine learning models with Python
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Graph Machine Learning : Take graph data to the next level by applying machine learning techniques and algorithms
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Practical Machine Learning on Databricks : Seamlessly transition ML models and MLOps on Databricks
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Embedded software : the works
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Hands-On Design Patterns and Best Practices with Julia : Proven solutions to common problems in software design for Julia 1.x
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Practical machine learning for data analysis using Python
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