implementation of machine learning

USAGE SUMMARY

The phrase "implementation of machine learning" is correct and usable in written English.
You can use it when discussing the process of putting machine learning algorithms or systems into practice within a specific context or application. Example: "The implementation of machine learning in our data analysis processes has significantly improved our efficiency and accuracy."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

4 human-written examples

The UFO web server has been built around an efficient implementation of machine learning techniques for protein sequence classification which have been described in [ 10- 12].

BMC Genomics

Implementation of machine learning to identify potential mutation was performed in MATLAB (MathWorks, Natick, MA).

ACS Chemical Biology

The objective of the paper is to study the implementation of machine learning based controller to control non-linear systems.

Measurement

Companies like Microsoft, Facebook and Google have already declared their intentions to build up infrastructure to support the implementation of machine learning in as many products and services as possible.

TechCrunch

Human-verified similar examples from authoritative sources

Similar Expressions

56 human-written examples

The proliferation of big data has forced us to rethink not just data processing frameworks, but implementations of machine learning algorithms as well.

Journal of Big Data

I think it's the future of machine learning.

Forbes

In Proceedings of the 22nd international conference of machine learning.

Journal of Cheminformatics

Deep learning is a type of machine learning.

Huffington Post

YJO gave suggestions on fundamental knowledge of machine learning.

BMC Genomics

We were motivated to create a league table of machine learning techniques to learn what is hot and what is not in the machine learning field.

Bioinformatics

Another example would be the use of machine learning techniques, including regression models, neural networks, support vector machines and others.

BMC Genomics

Expert writing Tips

Best practice

Clearly define the scope of the "implementation of machine learning" to avoid ambiguity. Specify which algorithms, systems, or processes are involved.

Common error

Avoid using "implementation of machine learning" without specifying the context or specific algorithms involved. Being too vague can reduce the clarity and impact of your statement.

Linguistic Context

The phrase "implementation of machine learning" functions as a noun phrase. Ludwig AI indicates that this phrase is used to describe the practical application of machine learning techniques, as seen in examples relating to building infrastructure and developing controllers.

Expression frequency: Uncommon

Frequent in

Science

60%

News & Media

20%

Formal & Business

20%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

In summary, the phrase "implementation of machine learning" is a grammatically correct and usable phrase for discussing the practical application of machine learning algorithms and systems. Ludwig AI confirms the phrase is well received. While not extremely common, it appears across scientific and news contexts. When using this phrase, be specific about the context and algorithms involved to maintain clarity. Consider alternatives like "machine learning deployment" or "application of machine learning algorithms" for different nuances. Overall, the phrase effectively communicates the process of putting machine learning into action.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

machine learning deployment

Replaces "implementation" with "deployment", focusing on the active launch and utilization of machine learning models.

application of machine learning algorithms

Focuses on the application aspect, specifying that it involves machine learning algorithms.

practical machine learning

Emphasizes the real-world practicality and application of machine learning techniques.

machine learning integration

Highlights the act of combining machine learning with existing systems or processes.

rollout of machine learning systems

Similar to deployment, emphasizes the phased introduction of machine learning systems.

machine learning execution

Focuses on the actual running and performing of machine learning tasks.

operationalization of machine learning

Describes the process of making machine learning models functional and integrated into business operations.

establishment of machine learning workflows

Highlights the creation and standardization of processes that involve machine learning.

real-world machine learning applications

Focuses specifically on applications in real-world scenarios.

machine learning in practice

Focuses on how machine learning is used and applied in a practical, hands-on way.

FAQs

How is "implementation of machine learning" different from "application of machine learning"?

"Implementation of machine learning" refers to the technical process of putting machine learning models into practice, while "application of machine learning" focuses on the specific use case or problem being solved.

What does the "implementation of machine learning" typically involve?

The "implementation of machine learning" involves several steps including data preparation, model selection, training, testing, deployment, and continuous monitoring to ensure optimal performance.

What are common challenges in the "implementation of machine learning"?

Common challenges include data scarcity, model overfitting, lack of interpretability, scalability issues, and ensuring ethical and responsible use.

What can I say instead of "implementation of machine learning"?

You can use alternatives such as "machine learning deployment", "practical machine learning", or "machine learning integration" depending on the context.