How AutoML can give companies, with few data scientists, a key advantage

Over the last 10 years, there have been rapid advances in machine learning and data analysis. Software solutions like Automated Machine Learning can automate the process of data preprocessing, hyperparameter tweaking, and selection of best performing or most predictive model. However advanced algorithms in autoML have allowed for more functionality, like feature selection for the best interpretable model, models for aggressive feature selection, or for survival analysis. Feature selection was a process that took months, and engineering effort by several data scientists, using traditional methods.
Scaling a data science practice is challenging, time-consuming, and expensive. Whether that be discovering treatments, repurposing drugs, or understanding what triggers a disease. With Automated Machine Learning, you can empower data analysts, software engineers, and BI professionals to build and benefit from predictive models, while acquiring knowledge from interpreting the outputs. Through Data Science automation, a life-sciences team can be more productive, while freeing up the time of a data scientist to focus on understanding the problem and the potential solution.