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ModelOps Is The Key To Enterprise AI


In the last two years, large enterprise organizations have been scaling up their artificial intelligence and machine learning efforts. To apply models to hundreds of use-cases, organizations need to operationalize their machine learning models across the organization. At the center of this scaling up effort is ModelOp, the company that builds solutions to scale the processes that take models from the data science lab into production. Even before their recent $6 million Series A funding led by Valley Capital Partners with participation from Silicon Valley Data Capital, they are already the leader providing ModelOps solutions to Fortune 1000 companies. ModelOps is a capability that focuses on getting models into 24/7 production.

Taking AI from the lab to the real world – Bestgamingpro


The business is keen to move AI from the lab to the field, where it will presumably usher in a new era of efficiency and profitability. But it turns out that AI behaves quite differently on the testbed than it does in the actual world, so this is not as simple as it seems. Overcoming the barrier between the lab and real-world applications is increasingly becoming the next important goal in the race to deploy AI. Because intelligent technology relies on a consistent stream of trustworthy data to work effectively, a controlled setting isn't always the best place to test software. With AI, the actual test is now an uncontrolled environment, and many models are failing.

AI/ML Is Dead. Long Live AI/ML


In recent years, large organizations have committed billions to AI/Machine Learning (AI/ML) investment. According to CIO Magazine, the retail and banking sectors estimated that their 2019 spend on AI/ML would be, cumulatively, in excess of $11.6 Billion. The Healthcare sector was estimating an investment of approximately $36 Billion by 2025. Even with these huge financial commitments, some analysts predict that 87% of AI/ML Projects will fail to deliver as promised or never make it into production. Of particular note is that the vast majority of AI/ML projects today are targeted for internal datacenter deployment.

9 Key Issues To Consider When Operationalize AI For Enterprises


This year, despite the challenges from the Covid-19 pandemic, large corporations in the financial industry are operationalizing their AI initiatives. Many mature organizations already have established processes. In the last few years, they've been implementing process workflows, software tools, and frameworks to quickly operationalize their models to capitalize on the changing business landscape. However, as the business environment changed during the Covid-19 pandemic, organizations observed changes in their models' underlying assumptions. The urgency to rapidly deploy new models in a controlled environment to account for the market risks and take advantage of new opportunities proved to be challenging.

Operationalizing AI


When AI practitioners talk about taking their machine learning models and deploying them into real-world environments, they don't call it deployment. Instead the term that's used is "operationalizing". This might be confusing for traditional IT operations managers and applications developers. Why don't we deploy or put into production AI models? What does AI operationalization mean and how is it different from the typical application development and IT systems deployment?