Advances in AI and ML are reshaping healthcare

Go from BI to AI in Minutes

Go from BI to AI in Minutes

Yes, it’s possible to make that magnificent leap. In the long and arduous journey to achieving end-to-end enterprise digital transformation, machine learning coupled with big data offers a stepping stone to graduate from Business Intelligence (BI) to Artificial Intelligence (AI).

An emerging trend in the digital age, AI-driven software will form the core driving force behind enterprise systems, serving as a key component of the enterprise business strategy. Nevertheless, machine learning will be part of the AI IT strategy going forward. In fact, it has been the case for CxOs for sometime now.

The fact is that business intelligence technologies which deliver historical data to provide insights and patterns, lack the ability to learn and improve. As a result, they require human intervention to infer insights and detect patterns. On the other hand, machine learning technologies deliver systems with the capability to predict business outcomes based on statistical models.

Machine learning involves systems that can learn from data. This improves business decision-making and helps with predicting outcomes.

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Machine learning methods are applied to a variety of business systems. They are broadly categorised below.

Supervised learning: It is used when a ‘fair volume’ of historical data is available to estimate or predict a business-specific indicator linked to outcomes. This indicator is termed as a predictor variable. Algorithms like linear regression, regression trees and decision trees are used for prediction. With this type of learning, models are trained using historical data, which is labelled in line with expected prediction or estimates. This labelled data is used by models to learn. Hence the term supervised learning.

Unsupervised learning: Unsupervised learning as the name suggests does not need any predictor variable or labelled data. It is used to find hidden or hard to decipher data patterns, or for clustering similar data. Algorithms like K-means can be used for unsupervised learning.

Semi supervised learning:This is a combination of the supervised and unsupervised learning methodology.



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