Jun Wu, Forbes – October 9, 2020 Most of this year, enterprises have been reviewing the lessons learned in the past few years from their Enterprise AI initiatives, i.e., what has worked, what hasn’t, and how to move forward to modernize their infrastructures and take...
Why do we need ModelOps for Better Model Risk Management?
Preeti Padma, IndustryWired – July 16, 2020
In the past few years, multinational companies and other institutes have been escalating their artificial intelligence and machine learning efforts. In order to apply models to several organizational applications, companies need to operationalize their machine learning models across the organization. While model-based automation has unlocked many avenues of enhanced productivity and profitability, managing models at scale has challenges of its own, along with designing efficient model operations (ModelOps), especially in the financial sector. Market Research Firm IDC says that only 35 percent of analytics models are used in business applications. This is because most organizations lack a systematic way to track the performance of the models they do use. Hence the consensus is model operationalization is the need of the hour.