In G Suite, machine learning models make the day-to-day work more efficiently by taking over the menial tasks.
According to the recent Google Study, an average worker spends usually spends only about 5 percent of her or his time with the actually coming up with next big idea and the remaining time was spent in the quick stand of formatting, analysis, tracking and in the other tasks and in all the scenarios the use the machine learning technique.
The time sinks which can affect the productivity of work in doing some of the tasks like formatting the documents, Email Management and creating the expense reports. All this are considered as “Overhead” in the Google as the time spent on all those tasks will not directly relate to the creative output.
Based on the data the Machine learning algorithms observe the examples and make the predictions. With the help of a Platform based one like G Suite, the machine learning was made very easy and the works can be done very efficiently by taking over the menial tasks like scheduling the meetings, suggesting the docs etc.
G Suite comprises Calendar, Hangouts, Gmail, Google+, Google Drive etc. One of the earliest machine learning use cases of G Suite was within the Gmail, moreover, Gmail used a rule-based system which is meant for the anti-spam team which would create the new rules to match the individual patterns. By using the machine learning technique the spam detection was improved to 99 percent.
To detect the spam Google uses TensorFlow and the other machine learning algorithms to continually regenerate the “Spam Filter” so the system can predict which emails are most likely the junk. The Machine Learning finds new patterns and rules and adapts far quicker than the previous manual systems and it is one of the greatest advantages for more than one billion Gmail users who can avoid the spam emails within their accounts.
Click Here for more:Google Products News
The main Purpose of G Suite is to accomplish more with its intelligent apps and to integrate the machine learning in the day-to-day work.