Presented by Modzy

In the world of artificial intelligence (AI) and machine learning (ML), as the technology advances, so too does the lexicon of terminology required to be conversant. Almost every day, there’s a new buzzword capturing the attention of the market, leaving the rest of us with yet another topic on our research agendas.

Recently, the attention has centered on “ModelOps,” or AI model operationalization. Gartner describes ModelOps as focused on the governance and life cycle management of AI and decision models, while enabling the retuning, retraining, or rebuilding of AI models — providing an uninterrupted flow between the development, operationalization, and maintenance of models within AI-based systems.

ModelOps also provides business leaders insight into model performance and outcomes in a transparent and understandable way that doesn’t require translation or explanation by data scientists or machine learning engineers.

This last piece, around how ModelOps brings business leaders into the conversation, underscores the importance of ModelOps. It might very well be the key to unlocking the secret to successfully deploying AI to enterprise scale. ModelOps, for its ability to enable transparency and trustworthiness in AI, is the next frontier for machine learning operationalization (MLOps), and offers a window into the future of enterprise AI.

MLOps and ModelOps: What’s the big deal?

In the past few years, we’ve seen the “hardest part” of AI shift from attracting the scarce data science talent to build powerful AI models, to figuring out how to deploy these models from the lab into production at scale. Training the models is one thing — there are a number of powerful tools that can help data science teams train, tune, identify, and promote the right model for a specific use case.

Then comes the really hard part, the handoff to the development team to turn your tool or application into one that’s powered by AI. This is the MLOps part of the process.

Data scientists, while adept at building powerful AI models, aren’t used to building scaled software applications. On the flip side, developers aren’t experts in ML and AI, and lack easy ways for building or embedding AI models into applications.

This is where MLOps tools are changing the game. By reducing the average time it takes to deploy AI models into production — nine months — into a matter of hours, significant time, financial, and other resource savings can be realized.

Equally valuable, MLOps tools also provide data science teams powerful AI management tools and dashboards to monitor how AI models are performing in real-time, detect model drift, quickly retrain models when needed, all while fitting neatly in their existing tech stack and working with the model training tools, languages, and frameworks they love best.

While MLOps tools are a breakthrough for data science teams, there is still a missing link between the teams on the ground building and deploying AI, and the IT leadership responsible for overseeing and managing it. That’s where ModelOps comes in, and why it has the potential to be so transformational.

With dashboards and reporting tailored for leaders and program managers, ModelOps provides transparency into how teams are deploying and using AI anywhere in the enterprise.

This new technology will truly shift the current state, helping organizations address any pockets of “shadow AI” where teams are building AI-enabled tools and applications outside of the purview of the IT organization. In many cases, these shadow efforts mean duplicitous efforts, wasted resources, no ability to control costs related to infrastructure usage, security risks, and the list goes on.

Fortunately, ModelOps tools address all of these challenges, and then some, particularly for their ability to provide transparency and explainability into AI-enabled outcomes. This is possibly the most transformative part of ModelOps tools, as their dashboards and reporting capabilities present AI information in a way that is understandable to non-technical leaders — a key factor in moving AI adoption forward.

ModelOps: Gamechanging technology for enterprise AI

To realize the groundbreaking benefits promised by AI, organizations must first be able to deploy AI models into production at scale. This means empowering their data science and development teams with MLOps tools. The next step in the journey is to make sure that leaders are along for the ride, and you can’t get to ModelOps without first satisfying the needs of MLOps.

Fortunately, tools like Modzy exist to help address the needs of all AI builders and stakeholders, and to really drive towards the future of enterprise AI. ModelOps will be transformational because of its role in providing transparency into AI usage across the enterprise for leaders, thus charting the next frontier for MLOps and enterprise AI.

Kirsten Lloyd is Head of Go-to-Market at Modzy.

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