The Application Programming Interfaces are like ready-made recipes that make programming easier for developers. It is utilized to digitize and automate monotonous processes, which ends up reducing the producing cost of an application or product. When specifically talking about machine learning and artificial intelligence, these APIs are commercially integrated into existing platforms as the ML APIs can trigger interaction with a code snippet, other APIs, and user-base.
There are various machine learning APIs that can help you expand your knowledge base and tap in the power of this technology to increase user experience.
10 Trending Machine Learning APIs
Built on Apache, this API allows scientists to offer predictive capabilities to machines. You can easily bundle it with MBLib, HBase, Apache Spark, Spray, and Elasticsearch. The API utilizes a template system which helps in developing machine learning system. The developers can modify this system according to their requirements.
The PredictionIO offers various features such as quick engine deployment, dynamic query responses in real-time, customizable templates, systematic processes for fast ML modeling, hassle-free data infrastructure handling, etc.
Language Processing API
The Geneea API uses raw information to perform analysis. This means that raw text, text from a URL, or text from a document can be analyzed by this API. Of course, developers can input additional data such as a particular domain, language used, etc. The Geneea API is used for discretization, correction, language, tagging, named entity recognition, topic detection, etc.
When Slack came into existence, organizations were able to use it for communication. It was certainly the most preferred APIs back then and even today. All corporate employees and professionals have used Slack at least once. With its natural language processing functionality, Slack can help in building a customized communication network which can integrate with chatbots.
The NuPIC API harness the power of C++ and Python and uses Numenta’s Cortical Learning algorithm. Utilizing this API empowers developers to build temporal inference, sparsely distributed representations, and online learning.
Sightcorp Face API
To make user interactions extremely engaging, the Sightcorp Face API works like a charm. It includes face detection, demographics of crowd, emotion analysis, cross-platform functioning, human attention analysis, and crowd analysis.
The AT&T Speech API helps developers to utilize speech-recognition in their applications. The API includes various features such as natural language understanding, speech transcription, speech recognition, and other related capabilities. It can help you convert speech to text file without much hassle.
BigML allows integration of AI models to a system which developers are building. It empowers developers to include predictive models such as unsupervised and supervised tasks to the application. All this can be achieved with standard HTTP standards, which make this API extremely helpful and useful.
There is a chance that your home automation, smart TV, cars, robotics, smartphones, and wearables are utilizing Wit.ai API for speech functionality. The Wit.ai gives a voice interface to many applications, which makes the system more engaging for users. There are complete start-up guides available for this API.
Anaconda is a secure, enterprise-ready API, which offers access to more than 700 packages. Its best feature is the ability to properly manage data science resources. You can additionally deploy projects to live notebooks, interactive data applications, and ML models.
indico is indeed the most preferred predictive analysis API. It gives you two choices: photograph evaluation and text evaluation. The photograph evaluation can be used for facial localization and facial emotion. The text evaluation can be used for emotion, sentiment, and engagement analysis.
With machine learning now being the part and parcel of our daily functioning, it is impossible to not utilize these APIs. It would be extremely difficult for the developers to write the code from the starting and remove the underlying bugs. Using these APIs directly help in adding a few functionalities in less time than anticipated.