Boosting your ROI with AutoML & Automatic Feature Engineering

If your business has started using AI/ML tools or just started to think about it, this blog is for you. Whether you are a data scientist, VP of data science or a line of a business owner, you are probably wondering how AI will impact your organization in various ways or why your current strategies are not working somehow. If you are not using AI/ML, very good chances are that your vendors, customers etc., are using it in some way already.

Machine Learning was developed in the last decade(s) to build great expert systems – to learn patterns from historical data and build models, so they can embed and operate in the real world without the need to construct exhaustive manual rules/code to take care of every possibility. Standard Machine Learning techniques requires a data scientist to try algorithms such as Deep Learning, XGBoost, Random Forest, etc., on historical data to extract the features that influence some known outcome, such as churn, adoption of a product, etc., The data scientists cleanses the data, does manual feature engineering (modify the columns of data to do some binning, encoding etc.,), tunes algorithms to maximize the accuracy of the models. Common uses of Machine Learning includes:

Artificial Intelligence (AI) is a much broader umbrella than Machine Learning – you can think of Machine Learning as an application of Artificial Intelligence. The lines blur all the time given Machine Learning is the foundation for AI, which keeps evolving every single day pushing the boundaries. For now, I’d like to call it AI/ML in this blog.

AI/ML can automate your business, remove inefficiencies and grease the wheels. Works well in the era of big data where you need to make expert decisions with a lot of data that comes in each day. Loan approvals, Churn prevention, Fraud detection, Product Recommendations, Targeted Marketing Campaigns, etc., can be automated and be explained away and accounted for using advanced tools like MLI (Machine Learning Interpretability).

To deploy AI/ML in your enterprise, a business needs to at-least invest in the following:

So, if this looks so cut and dry, why don’t most enterprises deploy 10s and 100s and 1000s of AI/ML projects in production – in the likes of the big players?

Delay in AI/ML projects are often misconstrued today to be primarily related to data cleansing, data prep, data availability, etc., These are just means to the end.