Machine learning workflow

As explained in LLMs in Production

A machine learning life cycle is the sequence of activities involved in creating, deploying, operating, and retiring machine-learning models.

A machine learning workflow is the life cycle from creating a model through deployment, operation, and retirement. It answers why the system exists; infrastructure answers how it runs. section 3.4

A prototype may live on one data scientist’s laptop, but LLM systems make that approach difficult: model storage, production complexity, and operational tooling demand sturdier infrastructure. section 3.4

A production-oriented workflow connects:

• Model creation, deployment, operation, and retirement. section 3.4
• Data stores, orchestrators, pipelines, streaming integrations, and container registries around the model. section 3.4
• DataOps and DevOps capabilities with machine-learning infrastructure. section 3.4

Figure 3.7 A high level view of an MLOps infrastructure with LLMs in mind. This diagram attempts to cover the full picture and the complexity of the many tools involved to make ML models work in production.

Use the infrastructure overview to trace the full production picture and the many tools supporting machine-learning models. section 3.4

How LLMs in Production covers it

How deep it goes

Contrasts life-cycle purpose with infrastructure mechanics but stops short of detailing each life-cycle phase.

Examples

Uses laptop-based scikit-learn systems, Jupyter Notebooks, and an LLM-oriented infrastructure diagram as concrete contrasts.

What you can do after

Evaluate machine-learning platforms by separating life-cycle needs from the infrastructure used to satisfy them.

How it connects

Connects MLOps with infrastructure, DataOps, DevOps, data stores, orchestrators, pipelines, and container registries.

liveBookmanning.com

Read LLMs in Production on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Distributed Machine Learning PatternsThe stages and activities involved in developing, deploying, operating, maintaining, and retiring machine learning models and systems.
• 

  liveBookmanning.com
  Machine Learning Platform EngineeringThe end-to-end sequence of activities through which a machine learning solution progresses from problem formulation and experimentation through deployment, operation, and retirement.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionThe end-to-end sequence of steps for defining, building, evaluating, deploying, and maintaining a machine learning system.
• 

  liveBookmanning.com
  Graph Algorithms for Data ScienceA machine learning workflow is a sequence of processing and modeling steps for training or applying machine learning models.
• 

  liveBookmanning.com
  Deep Learning with R, Third EditionA machine learning workflow is an organized sequence of activities for defining a problem, preparing data, developing a model, and deploying and maintaining the resulting system.
• 

  liveBookmanning.com
  The Art of AI Product DevelopmentA machine learning lifecycle is an iterative process for preparing data, developing models, evaluating results, and applying machine-learning outputs.

Compare how 7 books explain machine learning workflow →

Related concepts

• pipelineequivalent to · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
• monitoringhas part · Monitoring is the ongoing observation and measurement of a system’s behavior, state, or operational signals to assess its condition and detect changes or problems.
• traininghas part · Training is a process of developing or improving a person’s or model’s capabilities through instruction, experience, or adjustment using data.
• preprocessinghas part · Preprocessing is the preparation or transformation of data, content, or other inputs into a form suitable for a subsequent computational operation.
• data ingestionhas part · Data ingestion is the process of acquiring or receiving data from one or more sources and bringing it into a system for storage or processing.
• model developmenthas part · Model development is the process of designing, constructing, training, evaluating, and refining a computational or statistical model for a defined task.

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