How AutoML Puts the Power of AI in the Hands of Business Analysts

Infusing artificial intelligence and machine learning into business applications is not a trivial task. Especially, when it comes to enterprises dealing with mission-critical applications, integrating ML with existing applications becomes a challenging exercise.

From the time an organization decides to incorporate machine learning to the actual deployment of a fully-trained model, there are multiple stages involving different teams and individuals with disparate skills. It has to go through the workflow depicted in the below graphic.

A Look at the Machine Learning Pipeline

Data acquisition involves identifying and extracting data from a variety of data sources such as RDBMS, NoSQL databases, data warehouses, third-party sources etc. Enterprises rely on advanced ETL (extract, transform, load) tools to aggregate data from heterogeneous sources.

Data exploration will provide insights into acquired datasets, and helps the data engineering teams to assess the quality of data. This phase will help the team in finding hidden patterns, correlations, missing data points in an aggregated dataset.

Data preparation phase deals with cleansing the dataset. Missing data points may get dropped, existing columns may get split, multiple columns may get combined, and finally, the dataset turns into a complete and a valuable input source for the remaining stages of the workflow.

Read the entire article at The New Stack

Janakiram MSV is an analyst, advisor, and architect. Follow him on Twitter,  Facebook and LinkedIn.