How can small businesses integrate machine learning into their processes ... or should they even do so?
Machine learning integration depends entirely on the stability of the process and the improvement that can be achieved by analyzing vast amounts of ever-changing data and making the necessary adjustments. Writing small, standalone data science scripts in Python to incorporate machine learning is possible even for small businesses that may have only a few employees with the necessary proficiency in mathematics and programming.
However, embedding machine learning and data science scripts into the daily workflow of large numbers of employees requires the resources to maintain a production-grade infrastructure. Small business can experiment using commercially available platforms like BigML as a way of validating the improvement potential in order to make informed choices about the return on investment from building their internal competencies in this area.
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Written by Mohan Giridharadas | Founder and CEO at LeanTaaS
Widely recognized as an expert in lean principles, Mohan Giridharadas spent 18 years at McKinsey & Company (including six years as senior partner/director), where he co-created the lean service operations practice, ran the North American lean manufacturing and service operations practices and the Asia-Pacific operations practice.
He has helped numerous Fortune 500 companies drive operational efficiency with lean practices. As the founder and CEO of LeanTaaS, Mohan has worked closely with dozens of leading healthcare institutions including Stanford Health Care, UCHealth, UCSF, New-York Presbyterian and more. Mohan holds a B.Tech from IIT Bombay, a master's degree in computer science from Georgia Institute of Technology, and an MBA from Stanford Graduate School of Business.
He is on the Faculty of Continuing Education at Stanford University and UC Berkeley Haas School of Business. Mohan has been named by Becker's Hospital Review as one of the top entrepreneurs innovating in healthcare.
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