Business leaders can’t afford to ignore MLOps for AI

When GenAI exploded in popularity in 2022, a gold rush began with businesses everywhere scrambling to incorporate GenAI into as many processes as possible. In more recent months, however, that wave of hype has been tempered by a not-so-simple realization: implementing AI isn’t easy.

Most businesses will abandon at least 30% of GenAI projects after the proof-of-concept stage by the end of 2025, according to Gartner. But the potential rewards are great. Early adopters of GenAI are already experiencing high returns on their investments.

To increase your chances of succeeding with AI, it’s important to deploy a proven approach for implementing AI technology. This is where MLOps (machine learning operations) comes in. MLOps offers an agile, scalable solution to bridge the gap between successful model development and real-world implementation.

What is MLOps for AI?

Think of MLOps as the next frontier in AI—like DevOps but tailored for machine learning in AI systems. Like DevOps revolutionized software development by enabling collaboration between development and operations teams, MLOps bridges the gap between data scientists, developers, and operations teams to create an AI lifecycle.

This means faster model updates, continuous learning, and deployment at scale.

Unlike traditional methods, which often involve lengthy manual processes to update and redeploy models, MLOps emphasizes automation and agility. It helps ensure models stay relevant by continuously retraining them with new data.

For example, if a model predicting consumer demand becomes outdated, MLOps enables real-time adjustments based on updated information, accelerating the decision-making process.

MLOps helps AI adapt

The explosion of GenAI popularity illustrates the need for MLOps. As your organization gains more experience with GenAI, you're likely to come face-to-face with use cases that demand a robust MLOps framework.

MLOps can help your AI models avoid “drift,” where your model’s predictions and outputs became less aligned with reality due to changing data and evolving user interactions. Deploying MLOps can help you avoid the problem of relying on outdated insights, facing costly retraining efforts, and losing competitive advantages.

This situation mirrors what businesses encountered during the COVID-19 pandemic, where consumer behavior shifted almost overnight. ML models that had been trained on years of historical data couldn’t adapt fast enough to account for drastic changes like the surge in ecommerce, new consumer preferences for contactless experiences, and frequency of supply chain disruptions.

Both cases highlight a critical issue: without a flexible, continuous system for managing and updating AI models, your business risks relying on outdated insights and missing crucial opportunities.

MLOps mitigates that risk by offering a scalable, automated framework to help AI models quickly adapt to change—whether due to a global pandemic or the next wave of technological innovation.

MLOps for AI provides crucial benefits for your organization

With MLOps, you can create reproducible workflows, automate redundant tasks, and focus on innovation instead of getting bogged down by operational inefficiencies. This is critical if you’re looking to scale your AI models while managing the risks and complexities that come therewith.

Automate the AI lifecycle

With MLOps for AI, you can automate the entire AI lifecycle—from data preparation to model monitoring. This allows data scientists and engineers to focus on innovative tasks, such as fine-tuning algorithms, rather than manually managing model updates.

Accelerate time to market

MLOps enables faster iteration, meaning you can deploy new models more quickly. MLOps can help ensure your models are constantly learning and redeploying with real-time data, reducing downtime and increasing business agility. This capability is critical for businesses that depend on predictive analytics and insights.

Improve safe AI governance and compliance

MLOps incorporates governance protocols at every stage of the AI lifecycle. This helps ensure your models meet ever-evolving ethical standards and regulatory requirements. This is particularly important for heavily regulated industries such as finance, healthcare, and government, where data integrity is critical.

Enhance cost efficiency

By automating and optimizing the ML lifecycle, MLOps helps your business reduce costs associated with cloud infrastructure, human resources, and time lost to manual processes. By preventing model drift, MLOps also alleviates some of the cost of expensive AI model retraining.

Employ a platform-driven approach to implement MLOps for AI

Centific is a frontier AI data foundry platform company specializing in helping AI creators bring models and applications to life with accurate, contextualized, and versatile data.

We recognize that every organization’s AI journey is unique. That’s why we employ a tailored MLOps approach designed to accelerate your AI adoption and optimize your model performance.

We can help your business integrate MLOps into your existing workflows, which helps ensure smooth collaboration between data scientists, engineers, and IT teams. With reusable continuous integration/continuous delivery pipelines, enhanced security protocols, and ongoing governance, we can help ensure that your AI models remain secure, scalable, and ready to deliver real-world value.

Adopt MLOps for AI today

Don’t stifle your AI models with outdated information, future-proof your AI investments by ensuring your models remain accurate and relevant. By integrating MLOps into your AI strategy, you’re building the foundation for future success with GenAI.

Learn about the businesses working with Centific to accelerate AI deployment.