A developer’s skill can do wonders, it can make people think how magically things happen as expected. Machine learning is the next big thing in automation, it’s all about predicting outcomes and maximizing efficiency on the basis of consumer data. This data can be as diverse as how much a person could spend on a given day, what movies or shows someone would like based on previous choices and the likes.
The Google’s machine learning prediction API gives enough scalability compared to other machine learning platforms like Big ML, IBM Watson, Microsoft Azure machine learning and Amazon. It’s basically all about big data. Features like spam detection and automated spam removal come to exist based on the history of trends and patterns that is used to predict future moves. Also, it’s faster than the blink of an eye, predicting future trends in 1/5th of a second and gives you that reliability by replicating your data across multiple data centers using Google cloud storage.
Data scientists are the ones to figure out what all can be predicted with the use of an API and how prediction API can be used to study patterns based on available data. Even though it sounds limited for use in nature, Google still offers more than any other machine learning platforms. Free prediction quota for the first six months with as many as 100 predictions a day gives you enough room to predict what you like and what you want. Post six months, you only pay for what you use and nothing more which is similar to other platforms too.
Unlike other machine learning platforms, the prediction API is an advanced level API that monitors human trends and predicts future possible patterns. Few platforms are still yet to grow from translating to different languages, human activity recognition based on accelerometer or the enforcement of algorithmic restrictions giving the developers a limited ground to code.
Prediction API is the developmental root of facial recognition, spam filtering, speech recognition, smart tagging, product recommendations, and priority filtering. The core ideology of a prediction API is to leverage the user experience by providing the ease of access. Search ranking, Facebook Ads ranking, shopping recommendations, movie suggestions, spam classification are some popular examples of prediction API. One of the most used example is the efficiency of driving that detects location with time and a particular day of the week monitoring the movement. If it’s a weekday with a morning drive towards downtown, the person is probably heading to work or returning home if it’s evening. Recording locations based on time of the day help understand a person’s particular location like work or home.