Almost 70% of companies have reported an increase in their revenue after the adaptation of AI in 2020. Additionally, companies that use API have seen their share price increase by more than 12% relative to companies that don’t. Combining AI and API can unlock new potential for companies by simplifying the usage of AI models.
However, executives might have a lack of knowledge regarding use cases of AI API. Therefore, in this article, we will cover the top 3 use cases of AI APIs and provide examples of APIs that enable AI integration. Additionally, we will cover the impact that AI has on API testing.
1- Text & speech analysis
Natural language processing (NLP) is an AI-based technology that allows computers and machines to comprehend human speech and text. NLP API will enable developers to utilize the existing NLP models and platforms without the need to create an NLP. This is a significant benefit as developing NLP models is cumbersome and expensive as they require a substantial amount of data gathering & labeling.
General NLP benefits include:
If you are interested in use cases of NLP, read the Comprehensive Guide to Top 30 NLP Use Cases & Applications.
An example of AI API that use NLP is:
Google cloud natural language API
This API enables developers to apply natural language understanding (NLU) to their apps. It includes features such as :In Figure 1, you can see an example of this API in use:
Figure 1. Google cloud natural language API in use
Computer vision is an AI-enabled technology in which images and videos can be analyzed to retrieve meaningful information from them. Actions or recommendations can be made based on the results. Computer vision APIs enable using existing computer vision models instead of developing a computer vision model yourself, as developing computer vision models is expensive and time-consuming.
Computer vision has a variety of use cases in different industries such as:
An example of computer vision API is :
Microsoft Azure computer vision APIs
Developers can retrieve information from the images that they specify which will be analyzed by the Microsoft image processing algorithm. Some of the information that can be retrieved from their APIs are:
Visible brands in the image,
Description of the image,
Faces in the image and their sex and age,
Landmark location,
Celebrity detection.
3- Machine learning
Machine learning (ML) is a part of AI & computer science that focuses on using data & algorithms to simulate how humans learn so it can gradually increase the accuracy of the system.
APIs can be used to access ML models in order to implement them in analysis or applications. They provide a set of functions and tools that can be used in ML development. Machine learning has many use cases such as :
IBM Watson’s machine learning platform provides tools that can fully automate your training processes.
We have provided you with a data-driven list of more than 300 AI APIs that you can access here.
Effect of AI on API testing
API testing is an important aspect of API development as it increases the chances of API functioning as desired. However, API testing is a time-consuming task if done manually. AI-enabled testing can automate API testing which in turn can:
Reduce the cost of testing
Reduce the time of testing
Increase testing coverage
Sponsored
PULSE is an automated AI-enabled tool for API testing, provided by Testifi. PULSE can decrease the cost & effort of testing by 50%. Many reputable companies such as Amazon and BMW use Tesifi’s services.
You can check our list of top API testing tool providers here.
If you have questions about how to use AI API do not hesitate to contact us:
Arshia is a junior Industry analyst at AIMultiple. He received his bachelor’s degree in International trade & business from Bahçeşehir University, Istanbul in 2021. He has had experience as reporting & analysis specialist assistant following graduation.
Almost 70% of companies have reported an increase in their revenue after the adaptation of AI in 2020. Additionally, companies that use API have seen their share price increase by more than 12% relative to companies that don’t. Combining AI and API can unlock new potential for companies by simplifying the usage of AI models.
However, executives might have a lack of knowledge regarding use cases of AI API. Therefore, in this article, we will cover the top 3 use cases of AI APIs and provide examples of APIs that enable AI integration. Additionally, we will cover the impact that AI has on API testing.
1- Text & speech analysis
Natural language processing (NLP) is an AI-based technology that allows computers and machines to comprehend human speech and text. NLP API will enable developers to utilize the existing NLP models and platforms without the need to create an NLP. This is a significant benefit as developing NLP models is cumbersome and expensive as they require a substantial amount of data gathering & labeling.
General NLP benefits include:
If you are interested in use cases of NLP, read the Comprehensive Guide to Top 30 NLP Use Cases & Applications.
An example of AI API that use NLP is:
Google cloud natural language API
This API enables developers to apply natural language understanding (NLU) to their apps. It includes features such as :In Figure 1, you can see an example of this API in use:
Figure 1. Google cloud natural language API in use
Computer vision is an AI-enabled technology in which images and videos can be analyzed to retrieve meaningful information from them. Actions or recommendations can be made based on the results. Computer vision APIs enable using existing computer vision models instead of developing a computer vision model yourself, as developing computer vision models is expensive and time-consuming.
Computer vision has a variety of use cases in different industries such as:
An example of computer vision API is :
Microsoft Azure computer vision APIs
Developers can retrieve information from the images that they specify which will be analyzed by the Microsoft image processing algorithm. Some of the information that can be retrieved from their APIs are:
Visible brands in the image,
Description of the image,
Faces in the image and their sex and age,
Landmark location,
Celebrity detection.
3- Machine learning
Machine learning (ML) is a part of AI & computer science that focuses on using data & algorithms to simulate how humans learn so it can gradually increase the accuracy of the system.
APIs can be used to access ML models in order to implement them in analysis or applications. They provide a set of functions and tools that can be used in ML development. Machine learning has many use cases such as :
IBM Watson’s machine learning platform provides tools that can fully automate your training processes.
We have provided you with a data-driven list of more than 300 AI APIs that you can access here.
Effect of AI on API testing
API testing is an important aspect of API development as it increases the chances of API functioning as desired. However, API testing is a time-consuming task if done manually. AI-enabled testing can automate API testing which in turn can:
Reduce the cost of testing
Reduce the time of testing
Increase testing coverage
Sponsored
PULSE is an automated AI-enabled tool for API testing, provided by Testifi. PULSE can decrease the cost & effort of testing by 50%. Many reputable companies such as Amazon and BMW use Tesifi’s services.
You can check our list of top API testing tool providers here.
If you have questions about how to use AI API do not hesitate to contact us:
Arshia is a junior Industry analyst at AIMultiple. He received his bachelor’s degree in International trade & business from Bahçeşehir University, Istanbul in 2021. He has had experience as reporting & analysis specialist assistant following graduation.
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