How deeply does Generative AI in Action cover MLflow?

Explains core components and demonstrates tracking configuration and metrics logging, without detailing deployment internals.

MLflow

As explained in Generative AI in Action

MLflow is an open-source platform for managing machine-learning experimentation, reproducibility, model packaging, deployment, and lifecycle operations.

MLflow is an open-source platform for managing machine-learning experimentation, reproducibility, packaging, deployment, and lifecycle operations. In LLM applications, it supports observability by centralizing tracking, model management, and deployment across providers and cloud platforms. section 11.4.7

Its production-oriented capabilities include:

Packaging code, registering models, and deploying models through an MLflow server. section 11.4.7
Supporting debugging and operational analysis alongside logs, KPIs, and request tracing. section 11.4.7

Track an LLM experiment

Configure an MLflow tracking URI, create a GenAI experiment, call a chat-completion API, and record latency, prompt tokens, conversation tokens, and completion tokens for each response.

How Generative AI in Action covers it

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Also explained in

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Machine Learning Platform EngineeringMLflow is an open-source platform for managing the machine learning lifecycle, including experiment tracking, model packaging, registry, and deployment.
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Designing Deep Learning Systems: A guide for software engineersMLflow is an open-source platform for managing the machine learning lifecycle, including experiment tracking, artifact management, model registration, and deployment.

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Related concepts

experiment trackingused by · Experiment tracking is the systematic recording and organization of experiment configurations, metrics, artifacts, and results to support inspection, comparison, or reproducibility.
Weights and Biasesalternative to · Weights & Biases is a machine-learning development platform for tracking experiments and managing runs, metrics, artifacts, models, and related workflows.
model registryhas part · A model registry is a system for storing, organizing, versioning, and managing machine-learning models and their associated metadata throughout their lifecycle.
platformkind of · A platform is a foundational computing system, environment, or online service that provides capabilities or infrastructure for building, running, delivering, or interacting with applications and services.
MLflow clientused by · The MLflow client is a programmatic interface for tracking machine-learning runs, managing metadata and artifacts, and accessing registered models.

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