The power of MLOps to scale AI across the enterprise

To say that it’s challenging to achieve AI at scale across the enterprise would be an understatement. 

An estimated 54% to 90% of machine learning (ML) models don’t make it into production from initial pilots for reasons ranging from data and algorithm issues, to defining the business case, to getting executive buy-in, to change-management challenges.

In fact, promoting an ML model into production is a significant accomplishment for even the most advanced enterprise that’s staffed with ML and artificial intelligence (AI) specialists and data scientists.

Enterprise DevOps and IT teams have tried modifying legacy IT workflows and tools to increase the odds that a model will be promoted into production, but have met limited success. One of the primary challenges is that ML developers need new process workflows and tools that better fit their iterative approach to coding models, testing and relaunching them.

That’s where MLOps comes in: The strategy emerged as a set of best practices less than a decade ago to address one of the primary roadblocks preventing the enterprise from putting AI into action — the transition from development and training to production environments. 

Gartner defines MLOps as a comprehensive process that “aims to streamline the end-to-end development, testing, validation, deployment, operationalization and instantiation of ML models. It supports the release, activation, monitoring, experiment and performance tracking, management, reuse, update, maintenance, version control, risk and compliance management, and governance of ML models.”

Verta AI cofounder and CEO Manasi Vartak, an MIT graduate who led mechanical engineering undergraduates at MIT CSAIL to build ModelDB, co-created her company to simplify AI and and ML model delivery across enterprises at scale. 

Her dissertation, Infrastructure for model management and model diagnosis, proposes ModelDB, a system to track ML-based workflows’ provenance and performance. 

“While the tools to develop production-ready code are well-developed, scalable and robust, the tools and processes to develop ML models are nascent and brittle,” she said. “Between the difficulty of managing model versions, rewriting research models for production and streamlining data ingestion, the development and deployment of production-ready models is a massive battle for small and large companies alike.”

Model management systems are core to getting MLOps up and running at scale in enterprises, she explained, increasing the probability of modeling success efforts. Iterations of models can easily get lost, and it’s surprising how many enterprises don’t do model versioning despite having large teams of AI and ML specialists and data scientists on staff. 

Getting a scalable model management system in place is core to scaling AI across an enterprise. AI and ML model developers and data scientists tell VentureBeat that the potential to achieve DevOps-level yields from MLOps is there; the challenge is iterating models and managing them more efficiently, capitalizing on the lessons learned from each iteration. 

VentureBeat is seeing strong demand on the part of enterprises experimenting with MLOps. That observation is supported by IDC’s prediction that 60% of enterprises will have operationalized their ML workflows using MLOps by 2024. And, Deloitte predicts that the market for MLOps solutions will grow from $350 million in 2019 to $4 billion by 2025. 

Supporting MLOps development with new tools and workflows is essential for scaling models across an enterprise and gaining business value from them.

For one thing, improving model management version control is crucial to enterprise growth.