AI integration and MLOps

Once thought to be something only the companies with budgets significant enough to hire highly skilled data scientists, machine learning operations, or “MLOps,” has evolved into the mainstream.

“As AI adoption accelerates, MLOps will continue to play an even bigger role in enabling organizations to use AI and machine learning more effectively,” says Miroslav Klivansky, global practice leader of analytics and AI at Pure Storage.

That acceleration of MLOps is primarily driven by the fact that, to remain competitive, organizations are increasing their reliance on machine learning models to make critical business decisions — and with that rises the need for scalable and reliable machine learning models. MLOps (through the combination of machine learning, data engineering and DevOps) provides the collaboration, framework and tools necessary to operationalize ML models at scale.

The MLOps market is expected to grow considerably in the next few years. While roughly $1.1 billion in 2022, MarketsandMarkets predicts it will surge just over 41% annually through 2027 to reach $5.9 billion. A report from Grand View Research expects rapid growth to continue through at least 2030 when it forecasts the MLOps to reach roughly $17 billion by that year.

In addition to improving the scale and reliability of ML systems, MLOps also helps foster collaboration across all teams involved with machine learning, including data scientists, engineers, developers and operations. Hence, everything from model development to deployment runs more smoothly. MLOps should also help improve model governance through better version control and monitoring of the model while in use.

One of the core goals of MLOps is to accelerate the deployment of ML models into production environments. By automating and streamlining the building process, MLOps can speed model deployment and enable enterprises also to iterate and keep their models up to date rapidly. This keeps models accurate and relevant even when business requirements evolve and data changes.

Additionally, because MLOps emphasizes reproducibility and traceability, it helps maintain data versioning and model code and understands the heritage of their systems. This aids teams in auditing their models and debugging potential issues.

“MLOps can enable organizations to streamline the machine learning cycle to make it more efficient while reducing the time it takes to move from model development to deployment,” says Klivansky. “MLOps can also scale and adapt machine learning workflows by automating repetitive tasking and providing a structured framework for collaboration to address changes in machine learning systems quickly,” he says.

Shri Subramanian, AI/ML senior product manager at Datadog, says MLOps is critical for organizations to remain competitive today. “It streamlines the ML development lifecycle, enabling faster time-to-market, higher model quality and lower costs. With more and more companies adopting ML and AI within their businesses, you need a well-established set of practices to help manage and improve your models in production,” says Subramanian.

Olga Kupriyanova, principal consultant at research and advisory firm ISG, adds that MLOps helps drive scale. The MLOps framework is “paramount for organizations to efficiently scale machine-learning efforts and derive actionable insights from their data,” Kupriyanova says. They add that MLOps help to bridge the gap between the experimental nature of machine learning and the operational rigor required in production environments.

“By implementing MLOps, companies can accelerate the time-to-market of their AI-driven solutions, enhance model accuracy, and ensure that their machine-learning systems are robust, scalable and compliant with regulatory requirements. In the context of staying competitive, MLOps enables businesses to rapidly adapt to market changes, personalize customer experiences, optimize operational processes and innovate continually,” Kupriyanova says.

She adds that embracing MLOps isn’t merely an option but a necessity for organizations that wish to reach the full potential of their machine learning efforts and maintain market leadership.