Operationalizing Machine Learning Pipelines : Building Reusable and Reproducible Machine Learning Pipelines Using MLOps

Operationalizing Machine Learning Pipelines : Building Reusable and Reproducible Machine Learning Pipelines Using MLOps

Enregistré dans:

Détails bibliographiques
Auteur principal:	Pandey, Vishwajyoti. (Auteur)
Autres auteurs:	Bengani, Shaleen. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	New Delhi : BPB Publications, 2022.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

Machine Learning Engineering on AWS : Build, scale, and secure machine learning systems and MLOps pipelines in production
par: Lat, Joshua Arvin.
Publié: (2022)

Build, train, and deploy end-to-end machine learning and deep learning pipelines
par: Kolodiazhnyi, Kirill.
Publié: (2020)

Building ETL Pipelines with Python : Create and deploy enterprise-ready ETL pipelines by employing modern methods
par: Pandey, Brij Kishore.

Machine Learning and Deep Learning Algorithms : Tools and Techniques Using MATLAB and Python
par: Kumar Pandey, Abhishek.

CICD Pipeline with Docker and Jenkins : Learn How to Build and Manage Your CICD Pipelines Effectively
par: Rawat, Sandeep.

Machine Learning Engineering with Python : Manage the lifecycle of machine learning models using MLOps with practical examples
par: Mcmahon, Andrew P..

Machine Learning in Production : Master the art of delivering robust Machine Learning solutions with MLOps
par: Pote, Suhas.

Privacy-Preserving Machine Learning : A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
par: Aravilli, Srinivasa Rao.

Operationalizing Threat Intelligence : A guide to developing and operationalizing cyber threat intelligence programs
par: Wilhoit, Kyle.
Publié: (2022)

Simplified Machine Learning : The essential building blocks for Machine Learning expertise
par: Sharma, Pooja.

Statistics for Machine Learning : Implement Statistical methods used in Machine Learning using Python
par: Singh, Himanshu.

Underground Pipeline Corrosion
par: Orazem, Mark E..
Publié: (2014)

Machine learning : expert techniques to tackle complex machine learning problems using Python
par: Thanaki, Jalaj.
Publié: (2018)

Machine Learning for Beginners : Build and deploy Machine Learning systems using Python (English Edition) Ed. 2
par: Bhasin, Harsh (19..-....; informaticien).

Practical Machine Learning on Databricks : Seamlessly transition ML models and MLOps on Databricks
par: Sinha, Debu.

Hands-on Pipeline as Code with Jenkins : CICD Implementation for Mobile, Web, and Hybrid Applications Using Declarative Pipeline in Jenkins
par: Patil, Ankita.

Offshore Pipelines Design, Installation, and Maintenance
par: Guo, Boyun.
Publié: (2013)

Machine learning
par: Mueller, John Paul (1958-....).

Building Machine Learning Systems with Python : Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow
par: Coelho, Luis Pedro.
Publié: (2018)

Continuous Machine Learning with Kubeflow : Performing Reliable MLOps with Capabilities of TFX, Sagemaker and Kubernetes
par: Choudhury, Aniruddha.

Machine Learning on Kubernetes : A practical handbook for building and using a complete open source machine learning platform on Kubernetes
par: Masood, Faisal.
Publié: (2022)

Python Machine Learning Projects : Learn how to build Machine Learning projects from scratch
par: Vora, Deepali R..

Machine learning fundamentals : use Python and scikit-learn to get up and running with the hottest developments in machine learning
par: Saleh, Hyatt.
Publié: (2018)

Machine learning for Mobile : practical guide to building intelligent mobile applications powered by machine learning
par: Gopalakrishnan, Revathi.
Publié: (2018)

Machine Learning in Biotechnology and Life Sciences : Build machine learning models using Python and deploy them on the cloud
par: Alkhalifa, Saleh.
Publié: (2022)

Hands-on Pipeline as YAML with Jenkins : A Beginner's Guide to Implement CICD Pipelines for Mobile, Hybrid, and Web Applications Using Jenkins
par: Soni, Mitesh.

Scala machine learning projects : build real-world machine learning and deep learning projects with Scala
par: Karim, Rezaul Md..
Publié: (2018)

Machine Learning for Imbalanced Data : Tackle imbalanced datasets using machine learning and deep learning techniques
par: Abhishek, Kumar.

Guide to the practical use of chemicals in refineries and pipelines
par: Fink, Johannes Karl.
Publié: (2016)

Machine Learning with Python : Design and Develop Machine Learning and Deep Learning Technique using real world code examples
par: Vijayvargia, Abhishek.

Reliability and maintainability of in-service pipelines
par: Mahmoodian, Mojtaba.
Publié: (2018)

Pipeline Leak Detection Handbook
par: Henrie, Morgan.
Publié: (2016)

Hands-on automated machine learning : a beginner's guide to building automated machine learning systems using AutoML and Python
par: Das, Sibanjan.
Publié: (2018)

Machine learning in Java : helpful techniques to design, build, and deploy powerful machine learning applications in Java
par: Bhatia, AshishSingh.
Publié: (2018)

Building Machine Learning Systems with Python : master the art of machine learning with Python and build effective machine learning systems with this intensive hands-on guide
par: Richert, Willi.
Publié: (2013)

Comment saboter un pipeline
par: Malm, Andreas, 1977-
Publié: (2020)

Mastering machine learning with scikit-learn : learning to implement and evaluate machine learning solutions with scikit-learn
par: Hackeling, Gavin.
Publié: (2017)

Hands-on machine learning with Azure : build powerful models with cognitive machine learning and artificial intelligence
Publié: (2018)

Machine learning for cybersecurity cookbook : over 80 recipes on how to implement machine learning algorithms for building security systems using Python
par: Tsukerman, Emmanuel.
Publié: (2019)

Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications
par: Lantz, Brett.
Publié: (2013)

Université catholique de l'Ouest - Bibliothèques - 3 place André Leroy - BP10808 - 49008 ANGERS Cedex 1