As I speak with data scientists, especially those working in Global 1000 companies, many express concerns about their situation. In some sense, they're victims of their own success: Data scientists are producing models that are making substantial contributions to the business, and thus more and more models are being used in production applications. But as a result, data scientists face several challenges. The causes of both issues are very consistent across most organizations, and as such, lend themselves to straightforward solutions. This is good news for both data scientists and the organizations that they work for – provided that organizations act, and do so with some urgency.

Managing models in production is challenging. To optimize the value of Artificial Intelligence, AI models must improve efficiency in business applications or support efforts to make better decisions as they run in production. ModelOps is the key capability for scaling and governing enterprise AI initiatives across the organization and ensuring that the maximum value is obtained from such enterprise AI initiatives. This article will talk about the requirements for systems that should be put in place to support this ModelOps capability. We will be drawing examples from real cases that use advanced production enterprise systems to orchestrate and automate the operationalization of models throughout their life cycle for scalable ModelOps.

Managing models in production is challenging. To optimize the value of Artificial Intelligence, AI models must improve efficiency in business applications or support efforts to make better decisions as they run in production. ModelOps is the key capability for scaling and governing enterprise AI initiatives across the organization and ensuring that the maximum value is obtained from such enterprise AI initiatives. This article will talk about the requirements for systems that should be put in place to support this ModelOps capability. We will be drawing examples from real cases that use advanced production enterprise systems to orchestrate and automate the operationalization of models throughout their life cycle for scalable ModelOps.

Managing models in production is challenging. To optimize the value of Artificial Intelligence, AI models must improve efficiency in business applications or support efforts to make better decisions as they run in production. ModelOps is the key capability for scaling and governing enterprise AI initiatives across the organization and ensuring that the maximum value is obtained from such enterprise AI initiatives. This article will talk about the requirements for systems that should be put in place to support this ModelOps capability. We will be drawing examples from real cases that use advanced production enterprise systems to orchestrate and automate the operationalization of models throughout their life cycle for scalable ModelOps.

ModelOps has swooped in to make artificial intelligence (AI) accessible by anyone anywhere. Faced with staggering stats like the fact that "On average, organizations take nine months to develop AI initiatives from prototype to production" and "as of 2018 only 47% of all AI investments make it out of the lab," data scientists and developers needed an easier way to take models from their favorite machine learning (ML) workbenches to running them at scale in production. If you're not working in the data science or the AI industry, ModelOps is probably a new term. It's a relatively new technical field, so you're likely to find differing opinions which can create confusion. Therefore, it's necessary to clarify what is meant by ModelOps.

ModelOp, a leading provider of ModelOps solutions, announced it has been recognized in reports by industry analyst Gartner and included in the Forrester paper on the new and fast growing ModelOps market. As enterprises become increasingly reliant on AI models to help them transform and reimagine business, the challenges of managing AI models is on the rise. According to Forrester research report, organizations must employ new ModelOps capabilities if they want to operate AI models at scale. Gartner analysts report, "it is to be noted that ModelOps lies at the heart of any enterprise AI strategy." In addition, "ModelOps is about creating a shared service that runs across the organization – enabling robust scaling, governance, integration, monitoring and management of various AI models. Adopting a ModelOps strategy should facilitate improvements to the performance, scalability and reliability of AI models."

Clearly financial services organizations possess the impetus to take advantage of AI and ML capabilities, and yet models still aren't being deployed– which exposes a quagmire in the process of model deployment. Could it be they're focusing too much on the development aspect and ignoring the criticality of ModelOps? Model validation is required across all regulated industries, but FinServ institutions especially face significant regulatory compliance mandates from the federal government – placing yet another roadblock on their path to AI success. Given these same institutions leverage thousands of models per day, they must typically staff large teams across their model risk management program, including spinning up large teams of model validators. ModelOps refers to the process of enabling data scientists, data engineers, and IT operations teams to collaborate and scale models across an organization.

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.

My first post in this series introduced the concept of enterprise AI and listed five common misconceptions, the first of which was covered here in the second post. This post deals with the second misconception, which relates to an important but often misunderstood initiative called automated machine learning, or AutoML. As we'll see, AutoML holds real promise and will likely play an important role in enterprise AI, but the confusion caused by unqualified assertions about AutoML can lead to grief. Definitions of AutoML vary slightly, but most are variations on this one from Microsoft: "Automated ML democratizes the machine learning model development process, and empowers its users, no matter their data science expertise, to identify an end-to-end machine learning pipeline for any problem." In light of these worthy objectives, and in light of claims by many vendors that they're delivering AutoML, it's not surprising that many are excited by it's potential to make the benefits of AI ubiquitous.