Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách keo gáy, bìa mềm
Deploy, manage, and scale Machine Learning models
with MLOps effortlessly KEY FEATURES ● Explore several ways to build and
deploy ML models in production using an automated CI/CD pipeline. ●
Develop and convert ML apps into Android and Windows apps. ● Learn how
to implement ML model deployment on popular cloud platforms, including
Azure, GCP, and AWS. DESCRIPTION ‘Machine Learning in Production’ is an
attempt to decipher the path to a remarkable career in the field of
MLOps. It is a comprehensive guide to managing the machine learning
lifecycle from development to deployment, outlining ways in which you
can deploy ML models in production. It starts off with fundamental
concepts, an introduction to the ML lifecycle and MLOps, followed by
comprehensive step-by-step instructions on how to develop a package for
ML code from scratch that can be installed using pip. It then covers
MLflow for ML life cycle management, CI/CD pipelines, and shows how to
deploy ML applications on Azure, GCP, and AWS. Furthermore, it provides
guidance on how to convert Python applications into Android and Windows
apps, as well as how to develop ML web apps. Finally, it covers
monitoring, the critical topic of machine learning attacks, and A/B
testing. With this book, you can easily build and deploy machine
learning solutions in production. WHAT YOU WILL LEARN ● Master the
Machine Learning lifecycle with MLOps. ● Learn best practices for
managing ML models at scale. ● Streamline your ML workflow with MLFlow. ●
Implement monitoring solutions using whylogs, WhyLabs, Grafana, and
Prometheus. ● Use Docker and Kubernetes for ML deployment. WHO THIS BOOK
IS FOR Whether you are a Data scientist, ML engineer, DevOps
professional, Software engineer, or Cloud architect, this book will help
you get your machine learning models into production quickly and
efficiently. TABLE OF CONTENTS 1. Python 101 2. Git and GitHub
Fundamentals 3. Challenges in ML Model Deployment 4. Packaging ML Models
5. MLflow-Platform to Manage the ML Life Cycle 6. Docker for ML 7.
Build ML Web Apps Using API 8. Build Native ML Apps 9. CI/CD for ML 10.
Deploying ML Models on Heroku 11. Deploying ML Models on Microsoft Azure
12. Deploying ML Models on Google Cloud Platform 13. Deploying ML
Models on Amazon Web Services 14. Monitoring and Debugging 15.
Post-Productionizing ML Models
Thể loại:Computers - Artificial Intelligence (AI)
Năm:2023
Ngôn ngữ:english
Trang:659
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