MLOps is here to operationalize AI

When businesses initially begin using artificial intelligence and developing machine learning projects, they frequently focus on theory. Data scientists employ instruments to produce proofs of concept, but those don't work well in real-world applications. As a result, according to IDC research, deploying an artificial intelligence (AI) or machine learning (ML) solution can take a year on average. This is called "model velocity," or how much time it takes from start to finish.

MLOps is here to help in this situation. Machine learning operations, also known as MLOps, is a collection of best practices, frameworks, and tools businesses may use to manage data, models, deployment, monitoring, and other facets of implementing a theoretical proof-of-concept AI system.

Model velocity is reduced to weeks and occasionally days via MLOps. This is why MLOps is needed, just as DevOps accelerates the typical time to construct an application. MLOps' value proposition is as clear as it gets. Adopting MLOps enables businesses to build more models, innovate more quickly, and handle more use cases.

According to IDC, 60% of businesses will use MLOps to operationalize their ML operations by 2024. And when businesses were polled about the difficulties of adopting AI and ML, the absence of MLOps was the second-largest barrier behind the cost.

What is MLOps?

MLOps is a set of procedures to reliably and efficiently deploy and maintain machine learning models in production. The term combines "machine learning" and "DevOps," a continuous development methodology used in the software industry.

DevOps versus MLOps

Large-scale software systems are often developed and run using the DevOps methodology. Shortening development cycles, speeding up deployment, and ensuring reliable releases are a few advantages of this strategy. You incorporate two ideas into the creation of the software system to bring about these advantages: continuous integration (CI) and continuous delivery (CD).

Machine learning and other software systems are comparable in source control integration, unit testing, integration testing, and continuously delivering the software module or package. However, there are a few significant differences in ML.

CI now includes testing and verifying data, data schemas, and models in addition to code and component testing and validation. An ML training pipeline that should automatically deploy another service is what CD is now about, rather than a specific software package or service (model prediction service). The automatic retraining and serving of the models is the focus of a novel attribute called continuous training (CT), which is specific to ML systems.

What does MLOps have to offer?

MLOps is gradually becoming a stand-alone method for managing the ML lifecycle. It covers every lifecycle stage, including data collection, model building via software development, continuous integration and delivery, orchestration, deployment, health, diagnostics, governance, and business KPIs.

By developing more effective workflows, utilizing data analytics for decision-making, and enhancing customer experience, machine learning enables people and organizations to implement solutions that uncover previously untapped revenue streams, save time, and save costs.

Without a strong plan to adhere to, these objectives are challenging to achieve. Faster go-to-market times and lower operating costs result from automating model creation and deployment with MLOps. Managers and developers benefit from being able to make decisions more quickly and strategically.

MLOps acts as a road map to help individuals, small teams, and enterprises achieve their objectives despite obstacles like sensitive data, a lack of resources, a tight budget, etc. Because MLOps are not set in stone, you can make your map any size you desire. You can try out various options and keep what suits you.