Artificial Intelligence- A Growth Model to Humans

“An organization’s ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage”

-Jack Welch

Today not only machines learn, but also organizations do. There has been quite a scare on the advent of artificial intelligence and machine learning, replacing human centric jobs with machines. The present agony of people rushing to learn the new domain of data science for their survival can be countered by implementing AutoML in organizations of any size. AutoML is one of the newest tools in the industry, so organizations of any size and stage of automation excellence can flexibly assign employees with limited data science background to use various tools and platforms of AI and ML. AutoML makes AI and ML more enterprise user-friendly, fostering faster outputs.

Organizations must evaluate and choose AutoML tools purely on their requirements. There are various approaches to implement AutoML tools in an organization.

Build Your Own

Enterprise data science teams build platforms on their own, in this approach. Enterprises can hence control parameters, tuning, machine learning operations and model assessment through individual open source or proprietary code.

Commercial Platforms

Commercial platforms add some augmented AI capability, an extension of a traditional machine learning platform with automated machine learning capabilities. Organizations can have larger breath and depth of capability, with features to support collaboration between experts and non-experts. Products like Amazon SageMaker, RapidMiner and Atteryx fall into this category.

Augmented Platforms

Augmented platforms encourage citizen data scientists to build and deploy their own machine learning applications. These platforms are built to address specific problems and hence are less customized. Leaders like DataRobot, Aible, Big Squid and Tazi fall into this category.

Cloud Machine Learning Services

Cloud applications in machine learning are APIs helping developers integrate the existing applications with artificial intelligence. These APIs are purpose built and tend to focus on specific tasks like computer vision, language processing and translation. Vendors like Amazon Web Services (AWS), Google Cloud and Microsoft Azure fall into this category.

Organizations taking nascent steps towards their data science strategy could opt for augmented or commercial platforms. AutoML is an extension of the current machine learning strategy and not a replacement. Top management must analyze their platform capabilities around pattern modelling and automation specificity and match them to their current need. They must also evaluate the outcome of AutoML implementation in their organizations. Teams must streamline the processes of data integration and data washing through AutoML tools, as their first stage of AutoML implementation. They must streamline model development and deployment, as their second stage of AutoML implementation and finally streamline model testing and validation as their final stage of AutoML implementation. Organizations must learn and evolve to understand the dire need to enhance employee productivity and ease through machine learning, by augmenting human capability, but not human replacement!