deploy the model

USAGE SUMMARY

The phrase "deploy the model" is correct and usable in written English.
It is typically used in the context of machine learning or software development to refer to the process of making a model available for use in a production environment. Example: "After extensive testing, we are ready to deploy the model to our cloud infrastructure."

✓ Grammatically correct

TechCrunch

Journal of Big Data

BMC Systems Biology

The New Yorker

Data Science and Engineering

Journal of Cheminformatics

BMC Systems Biology

Journal of Big Data

The New Yorker

Data Science and Engineering

Human-verified examples from authoritative sources

Exact Expressions

1 human-written examples

While teams spend lots of energy developing a machine learning model, it's hard to actually deploy the model for customers to use.

TechCrunch

Human-verified similar examples from authoritative sources

Similar Expressions

59 human-written examples

Resource expense In this paper, we consider expense mostly in terms of time involved from setting up a cluster to deploying the model and maintaining it after the fact.

Journal of Big Data

Once all critical gaps necessary for in silico biomass formation were filled, we deployed the model to compute growth versus no-growth on different carbon, nitrogen, phosphorus, and sulfur sources at both 25-28°C 25-28°CC (anditional File 1).

BMC Systems Biology

The Administration then decided, Leverett said, to "deploy the Guantánamo model in Iraq"—to put aside its rules of interrogation.

The New Yorker

The Administration then decided, Leverett said, to "deploy the Guantánamo model in Iraq" — to put aside its rules of interrogation.

The New Yorker

In addition, researchers who want to deploy the CESM model for their research should go through all the steps of installation of the required software with appropriate configurations on different platform.

Data Science and Engineering

To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

Journal of Cheminformatics

Various ways for deploying the predictive model are sharing the model, score the model, incorporate the model in a BI report, embed the model in application [8].

Journal of Big Data

At 10, I marched to the top of the house, two bags of Thai takeout in hand, to deploy the late-model entertainment system in Richard Mishaan's atelier, provided by Philips Electronics.

The New York Times

We deploy the following regression models to observe the direction of causality between insurance market density and economic growth.

Financial Innovation

Such efforts of setting up scalable experiment environment and deploying the CESM model are challenging and time-consuming.

Data Science and Engineering

Expert writing Tips

Best practice

When writing about machine learning or software deployment, use "deploy the model" to clearly indicate the stage where a model transitions from development to active use in a production environment. Ensure the context clarifies where and how the model is being deployed.

Common error

Avoid using "deploy the model" without specifying the deployment environment (e.g., cloud, edge device). Providing context enhances clarity and prevents ambiguity about where the model is being utilized.

Linguistic Context

The phrase "deploy the model" functions as a verb phrase with a direct object. It describes the action of making a specific model available for use, which, according to Ludwig AI, is typically in a production environment. The verb "deploy" indicates the purposeful implementation of the model.

Expression frequency: Common

Frequent in

Science

35%

News & Media

35%

Formal & Business

15%

Less common in

Encyclopedias

5%

Wiki

5%

Academia

5%

Ludwig's WRAP-UP

In summary, the phrase "deploy the model" is a grammatically sound and commonly used expression, particularly within the realms of science, technology, and business. Ludwig AI confirms its typical use in production environments. Its function is to describe the act of making a model accessible for use. Related phrases such as "implement the model" or "put the model into production" provide alternative ways to express similar concepts. When using this phrase, it is important to give the context to avoid ambiguity and ensure clarity of communication.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

implement the model

Focuses on the practical application of the model in a system.

launch the model

Implies making the model available for use.

put the model into production

Highlights the transition from development to live use.

apply the model

Emphasizes the use of the model for a specific purpose.

roll out the model

Suggests a phased implementation across a wider system.

operationalize the model

Focuses on making the model part of routine operations.

integrate the model

Highlights combining the model with other systems or components.

utilize the model

Focuses on the active use of the model for its intended function.

execute the model

Implies running the model to generate results.

bring the model online

Focuses on making the model accessible and active in a network.

FAQs

What does it mean to "deploy the model" in machine learning?

In machine learning, to "deploy the model" means to make it available for use, typically in a production environment, so it can process data and make predictions or decisions.

What are some alternatives to saying "deploy the model"?

You can use alternatives like "implement the model", "launch the model", or "put the model into production", depending on the context.

How is "deploy the model" different from "train the model"?

"Train the model" refers to the process of teaching a machine learning model to make accurate predictions, while "deploy the model" refers to making that trained model available for real-world use.

What's involved in the process of deploying a machine learning model?

Deploying a machine learning model typically involves setting up the infrastructure, integrating the model with other systems, and ensuring it can handle real-time data processing and prediction tasks. It also includes monitoring its performance and retraining as needed.