Machine learning is the hottest cake in the market with a lot of applications in various domains. Unfortunately, machine learning algorithms have been daunting for people who are not so tech-savvy or data science experts.
Thanks to the Machine Learning APIs that make it easier for people to learn and apply machine learning methodologies. A Machine Learning API works just like any standard API by creating an abstraction layer for developers to integrate machine learning into the day-today applications that they develop. Let’s discuss the most common machine learning APIs used today.
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Common Machine Learning APIs for Data Science
1. Amazon Machine Learning API
Amazon Machine Learning API is built on the Amazon cloud platform. It simplifies the algorithms for making predictions that require lots of technical expertise on building the model, cleansing the data and performing statistical analysis.
The API also provides data visualisations based on the predictions. Other features of the Amazon Machine Learning API include creating UI permission levels, algorithmic restrictions, wizard-driven GUI. All these features, along with Amazon’s guarantee of simplicity and user-friendliness, has made Amazon Machine Learning API, the top choice of developers.
Popular use cases:
- Classifying the genre of the song by analysing the sound signal levels and features.
- Human Activity Recognition by analysing the sensor data captured from a gyroscope, smartphone or smartwatch. The API can tell whether the person is lying down, standing or sitting, walking upstairs or downstairs.
- Sales prediction by analysing the user activities during the first week or first-month.
- Detecting bots, fake users and spammers by examining website activity records.
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2. BigML
BigML is a very user-friendly RESTful API wrapped around machine learning algorithms. Users can build and run predictive models efficiently. The BigML API can be used for performing basic supervised and unsupervised machine learning tasks as well as creating machine learning pipelines that have extremely high levels of complexities.
Unlike many other proprietary APIs, BigML gives the users complete access to clusters, datasets, models, and anomaly detectors. Other features include providing a near real-time prediction, command-line interface and web interface.
Popular use cases:
- Creating what-if scenario analysis situations for business analysts by creating a descriptive model for relationships between the various attributes and properties in complex data
- creating applications that require periodic predictions. The old data can be stored on BigML platform and then can be re-used later.
3. Google Cloud APIs
Google Cloud API works on REST as well as RPC. Google Cloud APIs components like Vision API, Speech API and Natural Language API are most sought after for modern world applications. Vision API application includes reading printed and handwritten text, detecting faces and objects etc.
Developers can convert audio to text by using Google’s Cloud Speech API that working on powerful neural network models. Natural Language API is a powerful pre-trained model that helps developers work with natural language understanding like entity analysis, sentiment analysis, syntax analysis, etc.
Popular use cases:
- Ford uses Google’s Cloud API for tracks the driver to create a list of routes and places that the driver usually visits. This helps in predicting better navigation routes for the driver.
- Fraud detection can be easily done with Google APIs, and many companies give it off as a service to external customers.
4. Geneea Natural Language Processing API
Geneea Natural Language Processing API helps users to leverage the text data for natural language processing (NLP). It mainly offers four types of public APIs – General API (G3), VoC API, Media API and Intent Detection API. General API is a general-purpose API that performs sentiment analysis, language detection and other linguistic analyses.
The Media API helps the media industry to detect news articles are about, assigning special tags to editorials etc. The Voice of the Customer API (VoC ) helps users to analyse customer feedback, identifying the topics that customers are talking about, etc. the intent detector API helps to detect the intent of a text.
5. IBM Watson Discovery API
A powerful cognitive search and content analytics engine that allows developers to identify patterns and trends and other actionable insights. Such output from the API can be used to drive better decision-making.
The main components of Watson Discovery API include IBM Watson Personality Insights, IBM Watson Natural Language Processing, IBM Watson Assistant, IBM Watson Visual Recognition, IBM Watson Speech to Text, etc.
Popular use cases:
- Translating text to different other languages.
- Determining the popularity of a phrase or word with a predetermined audience.
- Making predictions of social characteristics of a person from the given text.
6. Kairos API
Kairos API is the simplest of all with a single main feature of face recognition. Users can incorporate face recognition in their software products very efficiently using the API. Its salient features include segregation of age groups, gender detection, diversity recognition, searching for matching faces, searching for human faces in photos and videos, etc.
7. Microsoft Azure Cognitive Service
This is mainly a Text Analytics API providing powerful natural language processing features over raw text. It is cloud-based providing a bunch of collection of AI and machine learning algorithms. The main features include key phrase extraction, language detection, sentiment analysis and named entity recognition.
These features are already being used in their own products like Bing and Xbox. But they are being released to customers only the recent past.
8. Prediction IO
PredictionIO is completely built on top of open-source machine learning server using open-source development methods and languages. The salient features include simplifying data infrastructure management, unifying data from multiple platforms, simplifying data infrastructure management, comprehensive predictive analytics etc. It also supports other data processing and machine learning libraries and OpenNLP and Spark MLLib.
9. TensorFlow API
TensorFlow API is the simplest and most efficient way of creating and executing TensorFlow graphs. The API is more language flavoured and is available in languages like C++, Go, Python, JavaScript and Swift. Of these developers prefer to use Python as it is more common and easy to use.
Conclusion
Machine learning is a vast and complex science and people have built libraries and API to make the developer’s life easier. We hope this article has given a good picture of the different machine learning APIs and the use cases of some of the common ones.
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Which is better to use—PyTorch or TensorFlow?
When it comes to speed, Pytorch is better as prototyping is quicker as compared to when you use Tensorflow. However, both Tensorflow and PyTorch help in increasing the speed of model development. If you require custom features for your neural network, you should opt for Tensorflow. If you are a newbie, learning Pytorch will be easier.
What is meant by Kairos ethnicity?
We do know that ancestry is related to your physical appearance. The Kairos app does have a feature that can recognize your ethnicity based on your looks. The diversity recognition feature in Kairo shoes recognizes the nuances and diversity or ethnicity with the help of your picture. You can get an estimate of your ethnic background when you upload your picture on the site.
What is the Flask API used for?
Python provides a micro web framework called Flask that assists in the development of web applications by offering functionality. It is classed as a micro framework since it does not necessitate the usage of any special tools or libraries. Flask is in charge of template rendering and HTTP request handling. If you want to create a basic online application, you should certainly utilize flask. It is also one of the simplest Python frameworks to learn.
