The concept of ‘Operationalizing AI’ revolves around finding the real value for your organization by infusing AI in applications that drive intelligent actions. The vast majority of enterprises now use AI to scale quickly and inexpensively, and the enterprises that do not adapt are at a distinct disadvantage. Finding meaningful, and actionable, outcomes from data can be difficult and slow. Automated Machine Learning rapidly creates powerful machine learning models that can infuse AI within applications that gives your enterprise the ability to move quickly, and doesn’t require an army of data scientists.
A free one-day jumpstart session with an experienced data architect and AI solutions specialist to discuss, and review how AI is being applied in enterprises.
Organizations capture and collect volumes of Big Data, but most of it is never sifted through or analyzed, ultimately serving no purpose.
A customer experience led digital transformation roadmap allows organizations to focus on the types of insights and next best actions that will deliver differentiated experiences. These insights can then be mapped to specific data requirements. One of the advantages of the Big Data architecture is that
Hiring a team of data scientists to build and deploy machine learning models is time-consuming, difficult, and costly.
The emerging area of Automated Machine Learning is introducing tools like DataRobot that democratize and scale data science to multiple skill levels within the organization.
Reduced turnaround time of prescription delivery and increased prescription accuracy is necessary to improve patient satisfaction. With human users working the queue, it cannot be ensured that improvement will be seen. The process must be made more efficient and uniform, and no two users work the same.
We worked created an efficient future-state design with RPA as well as a cognitive algorithm from historical data which standardized drug names and doctor directions. We regularly met with the client as we enhanced our algorithm to present metrics and gather feedback for improvement.