How deeply does The Art of AI Product Development cover mlops engineer?

Defines responsibilities and role boundaries, but stops short of procedural implementation or infrastructure configuration details.

Mlops engineer

As explained in The Art of AI Product Development

An MLOps engineer is a practitioner who operates and automates the infrastructure and workflows supporting machine-learning systems across their lifecycle.

An MLOps engineer manages the end-to-end machine-learning lifecycle, automating workflows so AI systems can be developed and operated at scale. The role centers on infrastructure and repeatable processes rather than solely building models. section 12.1.2

The role connects several delivery responsibilities:

Automate workflows for training, evaluation, integration, and deployment. section 12.1.2
Set up and manage infrastructure for experiments, models, and data. section 12.1.2
Work alongside machine-learning engineers, AI engineers, data engineers, and prompt engineers, whose responsibilities emphasize models, product integration, data systems, and prompts respectively. section 12.1.2

How The Art of AI Product Development covers it

Cover of The Art of AI Product Development

liveBook manning.com

Read The Art of AI Product Development on liveBook Get the book at manning.com

Related concepts

machine learning infrastructureconfigures · Machine learning infrastructure is the hardware, software, data, and services supporting the development, training, deployment, and operation of machine-learning systems.
machine learning workflowuses · A machine learning workflow is an organized sequence of activities for formulating, developing, evaluating, deploying, operating, and maintaining a machine learning system.
machine learning engineerused by · A machine learning engineer is a practitioner who develops, trains, deploys, operates, and improves machine-learning models and systems.
mlopsimplements · MLOps is the discipline of applying engineering and operational practices to develop, deploy, monitor, maintain, and manage machine-learning systems throughout their life cycle.

This explainer was generated by Manning's knowledge graph from the text of The Art of AI Product Development. The section links point to the parts of the book it draws on.