In this Digitalage, every organization is trying to apply machine learning and artificial intelligenceto their internal and external data to get actionable insights which will help them to be closer to today’s customer.
A few years back it was the field only for data scientistsand statisticians, who used to analyze the data, apply several techniques and provide results.
Today many of the organizations are using APIsto access the ready-made algorithms available in the market as they make it easy to develop predictive applications.In fact, you don’t even need to have an in-depth knowledge of coding or computer science to introduce them into your apps.
APIs provide the abstraction layers for developers to integrate machine learninginto real world applications without worrying about which technique to use or how to scale the algorithm to their infrastructure.
These APIs can be categorized broadly into 5 groups:
- Image and Face Recognition: It understands the content of the image, classifies the image into various categories, detects individual objects and faces, detects labels and logos from the images.
- Language Translation: Translate text between thousands of languages, allows you to identify in which language any text that you need to analyze was written. Some APIs allows organizations to communicate with the customer in their language.
- Speech Recognition and Conversion: Today most of the customer service is handled byChatbotswith underlying APIs helping simple question and answer. Speech to text APIs are used to convert call center voice calls into text for further analysis.
- Text /Sentiment Analytics usingNLP: With the rise of Social Media, consumers easily express and share their opinions about companies, products, services, events etc. Companies are interested in monitoring what people say about their brands in order to get feedback or enhance their marketing efforts. These APIs can identify, analyze, and extract the main content and sections from any web page. They further help in to analyze unstructured text forsentiment analysis, key phrase extraction, language detection and topic detection. There are some tools also helps in spam detection.
- Prediction: These APIs, as the name suggests helps topredictand find out patterns in the data. Typical examples are Fraud detection, customer churn,predictive maintenance, recommender systems and forecasting etc.
Google Cloud, Microsoft Cognitive Services, Amazon Machine Learning APIs & IBM Watson APIs are the leaders in the market.
With growing number of free/reasonably priced APIs and tsunami of data generated every day, the race is on as to which is the best Machine Learning API.
These machine learning APIs are not yet perfect or matured and they will take some time to learn and act accurately. But they allow faster time to market-based on ready availability, ratherthan asking data scientist to code the algorithms.
In future, machine learning will lead to revolutions that will intensify human capabilities, assist people in making good choices and help navigate through the world in powerful ways, like Iron Man’s Jarvis.