- Main
- Computers - Artificial Intelligence (AI)
- Practical MLOps: Operationalizing...
Practical MLOps: Operationalizing Machine Learning Models
Noah Gift, Alfredo DezaAvez-vous aimé ce livre?
Quelle est la qualité du fichier téléchargé?
Veuillez télécharger le livre pour apprécier sa qualité
Quelle est la qualité des fichiers téléchargés?
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.
Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.
You'll discover how to:
• Apply DevOps best practices to machine learning
• Build production machine learning systems and maintain them
• Monitor, instrument, load-test, and operationalize machine learning systems
• Choose the correct MLOps tools for a given machine learning task
• Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.
You'll discover how to:
• Apply DevOps best practices to machine learning
• Build production machine learning systems and maintain them
• Monitor, instrument, load-test, and operationalize machine learning systems
• Choose the correct MLOps tools for a given machine learning task
• Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Catégories:
Année:
2021
Edition:
1
Editeur::
O'Reilly Media
Langue:
english
Pages:
450
ISBN 10:
1098103017
ISBN 13:
9781098103019
Fichier:
PDF, 75.15 MB
Vos balises:
IPFS:
CID , CID Blake2b
english, 2021
Lire en ligne
- Télécharger
- pdf 75.15 MB Current page
- Checking other formats...
- Convertir en
- Débloquer la conversion des fichiers de plus de 8 MoPremium
Vous souhaitez ajouter une librairie ? Contactez-nous à support@z-lib.do
Le fichier sera envoyé à votre adresse de courriel dans 1 à 5 minutes.
Dans 1-5 minutes, le fichier sera delivré à votre compte Telegram.
Note : Assurez-vous que vous avez lié votre compte au bot Telegram de Z-Library.
Dans 1-5 minutes, le fichier sera delivré à votre appareil Kindle.
Remarque: vous devez valider chaque livre avant de l'envoyer à Kindle. Veuillez vérifier votre messagerie pour voir le mail avec la confirmation par Amazon Kindle Support.
La conversion en est effectuée
La conversion en a échoué
Avantages du statut Premium
- Envoyez aux e-lecteurs
- Limite de téléchargement augmentée
- Convertissez des fichiers
- Plus de résultats de recherche
- Autres avantages