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MLOps with Red Hat OpenShift

You're reading from   MLOps with Red Hat OpenShift A cloud-native approach to machine learning operations

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781805120230
Length 238 pages
Edition 1st Edition
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Authors (2):
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Ross Brigoli Ross Brigoli
Author Profile Icon Ross Brigoli
Ross Brigoli
Faisal Masood Faisal Masood
Author Profile Icon Faisal Masood
Faisal Masood
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Toc

Table of Contents (13) Chapters Close

Preface 1. Part 1: Introduction FREE CHAPTER
2. Chapter 1: Introduction to MLOps and OpenShift 3. Part 2: Provisioning and Configuration
4. Chapter 2: Provisioning an MLOps Platform in the Cloud 5. Chapter 3: Building Machine Learning Models with OpenShift 6. Part 3: Operating ML Workloads
7. Chapter 4: Managing a Model Training Workflow 8. Chapter 5: Deploying ML Models as a Service 9. Chapter 6: Operating ML Workloads 10. Chapter 7: Building a Face Detector Using the Red Hat ML Platform 11. Index 12. Other Books You May Enjoy

Monitoring ML models

Observability is a concept primarily used in the context of systems engineering, computer science, and monitoring complex systems. It refers to the ability to understand and infer the internal state and behavior of a system by examining its external outputs or observables. In simpler terms, it’s about gaining insight into how a system operates and performs by observing its outputs or responses.

Monitoring is one of the subjects of observability. It focuses on tracking and measuring predefined metrics and thresholds to ensure that systems and services are running within the expected parameters. It is also referred to as telemetry, akin to how real-time metrics data is collected in mission-critical operations such as launching a rocket to the moon. Unlike logging, which focuses on collecting event data for auditing and troubleshooting at a later date, monitoring focuses on real-time events and is focused on metrics information. For example, logging data...

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