Machine Learning Engineering on AWS : Build, scale, and secure machine learning systems and MLOps pipelines in production

Machine Learning Engineering on AWS : Build, scale, and secure machine learning systems and MLOps pipelines in production

Enregistré dans:

Détails bibliographiques
Auteur principal:	Lat, Joshua Arvin. (Auteur)
Support:	E-Book
Langue:	Anglais
Publié:	Birmingham : Packt Publishing, 2022.
Autres localisations:	Voir dans le Sudoc
Accès en ligne:	Accès à l'E-book

Documents similaires

Machine Learning with Amazon SageMaker Cookbook : 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
par: Lat, Joshua Arvin.
Publié: (2021)

Operationalizing Machine Learning Pipelines : Building Reusable and Reproducible Machine Learning Pipelines Using MLOps
par: Pandey, Vishwajyoti.
Publié: (2022)

Automated Machine Learning on AWS : Fast-track the development of your production-ready machine learning applications the AWS way
par: Potgieter, Trenton.
Publié: (2022)

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

Building and Automating Penetration Testing Labs in the Cloud : Set up cost-effective hacking environments for learning cloud security on AWS, Azure, and GCP
par: Lat, Joshua Arvin.

Cloud Native AI and Machine Learning on AWS : Use SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI services
par: Rangarajan, Premkumar.

Mastering Machine Learning on AWS : advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow
par: Mengle, Saket S.r..
Publié: (2019)

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

Engineering MLOps : Rapidly build, test, and manage production-ready machine learning life cycles at scale
par: Raj, Emmanuel.
Publié: (2021)

Machine Learning in the AWS Cloud : Add Intelligence to Applications with Amazon SageMaker and Amazon Rekognition
par: Mishra, Abhishek.

Machine Learning Engineering with MLflow : Manage the end-to-end machine learning life cycle with MLflow
par: Lauchande, Natu.
Publié: (2021)

Machine learning with Core ML : an iOS developer's guide to implementing machine learning in mobile apps
par: Newnham, Joshua.
Publié: (2018)

Data Engineering with AWS : Acquire the skills to design and build AWS-based data transformation pipelines like a pro
par: Eagar, Gareth.

Learning AWS IoT : effectively manage connected devices on the AWS cloud using services such as AWS Greengrass, AWS button, predictive analytics and machine learning
par: Kurniawan, Agus.
Publié: (2018)

Learning AWS
par: Sarkar, Aurobindo.
Publié: (2015)

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

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)

Machine Learning at Scale with H2O : A practical guide to building and deploying machine learning models on enterprise systems
par: Keys, Gregory.
Publié: (2022)

Large scale machine learning with Python
par: Sjardin, Bastiaan.
Publié: (2016)

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

Learning AWS : design, build, and deploy responsive applications using AWS cloud components
par: Sarkar, Aurobindo.
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)

Mastering Azure Machine Learning : Execute large-scale end-to-end machine learning with Azure
par: Körner, Christoph.
Publié: (2022)

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

Pragmatic Machine Learning with Python : Learn How to Deploy Machine Learning Models in Production
par: Nag, Avishek.

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

Azure Machine Learning Engineering : Deploy, fine-tune, and optimize ML models using Microsoft Azure
par: Fakhraee, Sina.

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

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)

Machine Learning Security with Azure : Best practices for assessing, securing, and monitoring Azure Machine Learning workloads
par: Kalyva, Georgia.

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

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

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

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

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

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

Machine Learning with LightGBM and Python : A practitioner's guide to developing production-ready machine learning systems
par: Wyk, Andrich Van.

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

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)

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