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Showing results for "Mlops"
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Introducing MLOps: How to Scale Machine Learning in the Enterprise
Practical MLOps: Operationalizing Machine Learning Models
Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples
Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
Implementing MLOps in the Enterprise: A Production-First Approach
Accelerated DevOps with AI, ML & RPA: Non-Programmer’s Guide to AIOPS & MLOPS
Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale
MLOps Engineering at Scale
The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI
Beginning MLOps with MLFlow: Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure
Machine Learning Engineering on AWS: Build, scale, and secure machine learning systems and MLOps pipelines in production
Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks
The Machine Learning Solutions Architect Handbook: Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI
Cloud Native AI and Machine Learning on AWS: Use SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI services (English Edition)
Continuous Machine Learning with Kubeflow: Performing Reliable MLOps with Capabilities of TFX, Sagemaker and Kubernetes (English Edition)
