machine learning deployment

USAGE SUMMARY

The phrase "machine learning deployment" is correct and usable in written English.
It can be used when discussing the process of implementing machine learning models into production environments. Example: "The team is focused on the machine learning deployment to ensure the model performs well in real-time applications."

✓ Grammatically correct

Science

News & Media

Formal & Business

Human-verified similar examples from authoritative sources

Similar Expressions

60 human-written examples

The company decided to make this a standard and to open source it to try and move machine learning model deployment forward.

TechCrunch

Open source has been all the rage in machine learning, but deployment and model/data collaboration are still a major challenge that commercial vendors like Algorithmia, Dataiku, and Pachyderm are working to solve with a cloud native and serverless bent.  .

TechCrunch

Identified criteria are grouped into five categories: correlation between proxy IP address and data location, landmark involvement in the verification process, measurements collecting, machine learning, and PDP protocol deployment.

Journal of Cloud Computing

Oracle, a company not exactly known for having the best relationship with the open source community, is releasing a new open source tool today called Graphpipe, which is designed to simplify and standardize the deployment of machine learning models.

TechCrunch

"Graphpipe is what's grown out of our attempt to really improve deployment stories for machine learning models, and to create an open standard around having a way of doing that to improve the space," Abrams told TechCrunch.

TechCrunch

Based on this, they can then use this data to build their data pipelines for deployment to BigQuery or create machine learning models, for example.

TechCrunch

That may be specific networking functions for the many telecoms that operate OpenStack deployments, or specific hardware for machine learning workloads.

TechCrunch

To successfully embed statistical machine learning models in real world applications, two post-deployment capabilities must be provided: (1) the ability to solicit user corrections and (2) the ability to update the model from these corrections.

Artificial Intelligence

Any incompatibility from any stage of the machine learning development process — from data processing to training to deployment to production infrastructure — can introduce error.

TechCrunch

Machine learning engineering happens in three stages — data processing, model building and deployment and monitoring.

TechCrunch

Sensor deployment problems have been studied in a variety of fields, including machine learning, robotics, computer vision, and computational geometry [10 18].

Human-centric Computing and Information Sciences

Expert writing Tips

Best practice

When discussing the practical implementation of machine learning models, use the phrase "machine learning deployment" to emphasize the transition from research to real-world application.

Common error

Avoid using "machine learning deployment" when referring to the initial stages of model development. This phrase is most appropriate when discussing the process of making a model available for use in a production environment.

Linguistic Context

The phrase "machine learning deployment" functions as a noun phrase, typically used as a subject or object in a sentence. It refers to the process or act of putting machine learning models into production environments, making them available for real-world use. Ludwig AI indicates that this is a correct and usable phrase.

Expression frequency: Missing

Frequent in

Science

33%

News & Media

33%

Formal & Business

33%

Less common in

Science

0%

News & Media

0%

Formal & Business

0%

Ludwig's WRAP-UP

In summary, the phrase "machine learning deployment" refers to the process of integrating machine learning models into real-world applications. Ludwig AI confirms the phrase is grammatically correct and suitable for professional and technical contexts. While examples of its usage are limited, alternative phrases such as "ML model deployment" or "deploying machine learning models" can be used depending on the specific context. Understanding the nuances of this term can help to accurately describe and discuss the practical application of machine learning in various domains.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

ml model deployment

Uses the abbreviation "ML" for "machine learning", shortening the phrase.

deploying machine learning models

Focuses on the action of deploying, emphasizing the process.

machine learning model serving

Highlights the aspect of making models available for use.

operationalizing machine learning

Emphasizes the process of making machine learning models operational and ready for business use.

implementing machine learning models

Focuses on the implementation aspect of machine learning.

machine learning integration

Highlights the integration of machine learning into existing systems.

machine learning model implementation

Focuses on the practical realization of a machine learning model.

automation of machine learning models

Emphasizes the automation aspect in the context of machine learning.

applying machine learning algorithms

Stresses the application of machine learning algorithms in real-world scenarios.

putting machine learning into production

Informal way of saying the process of making the model available in the production environment.

FAQs

How do you use "machine learning deployment" in a sentence?

You can use "machine learning deployment" to describe the process of integrating a trained machine learning model into a production environment. For example: "The team is working on the "machine learning deployment" to improve real-time predictions."

What's the difference between "machine learning deployment" and "machine learning implementation"?

"Machine learning implementation" refers to the actual coding and building of the machine learning model, while ""machine learning deployment"" focuses on making the model available and operational in a production setting.

What are some alternatives to "machine learning deployment"?

Alternatives include "ML model deployment", "deploying machine learning models", or "machine learning integration", depending on the specific context you want to emphasize.

Why is "machine learning deployment" important?

"Machine learning deployment" is critical because it bridges the gap between model development and real-world application, allowing organizations to leverage the insights and predictions generated by machine learning models to improve decision-making and automate processes.