“Augmented analytics has emerged as one of the most transformational innovations in data science and machine learning. It helps expert and citizen data scientists more quickly build and deploy models. Data and analytics leaders need to understand the benefits and limitations of augmented DSML.”*
As a labor and intellect-intensive process, it’s no secret that data science is desperate for both automation and augmentation. As such, the term AutoML, has become one of the most commonly used (and improperly used) terms in the world of technology and it’s very likely the endless buzz about AutoML has been incessantly illuminating your news streams and LinkedIn feed.
Unfortunately, the focus is frequently only placed on automation and augmentation used to aid the model creation process. Not enough attention is paid to the other steps in data science workflows that can be streamlined for experts and simplified for everyone else. Data prep, data exploration, model validation, model deployment and model monitoring are all examples of steps that can benefit from smoother, guided experiences.
There’s also a great deal of misinformation on the one-size-fits all nature of AutoML. Most AutoML is suited for basic use cases and doesn’t provide the fine-tuning capabilities to support more complex, real-world requirements.
The Gartner report ‘How Augmented Machine Learning is Democratizing Data Science’ clears up a lot of these misconceptions and fills common knowledge gaps. Download the report and we believe you will learn:
How your organization can benefit from augmented analytics
How automation and augmentation can be applied to more than just model creation
Why you should carefully select an augmented analytics offering that supports complex, real-world use cases