Leveraging machine learning processes to revolutionize the adoption of AI

By Amaresh Tripathy, Global Analytics Business Leader, Genpact

Since the rise of digitalization in the post pandemic world, the role of Artificial Intelligence (AI) and Machine Learning (ML) in driving digital business transformation has greatly increased. Enterprise leaders are accelerating digital initiatives at an unprecedented rate across industries, transforming how people live and work. However, as these programmes take shape, it is observed that only around half of all AI proofs of concept make it to production. For most teams, realizing their AI vision is still a long way off.

The push to move to cloud, as well as the expanding number of machine learning models, that witnessed tremendous growth during the pandemic, is projected to continue in the future. However, while operationalizing Artificial Intelligence, it has been found that merely 27% of the projects piloted by organizations successfully move to production.

What is Machine learning Operations all about?
Machine learning operations (MLOps) is all about how to effectively manage data scientists and operations resources to allow for successful development, deployment, and monitoring of models. Simply put, MLOps assist teams in developing, deploying, monitoring, and scaling AI and ML models in a consistent manner, reducing the risks associated with not having a framework for long-term innovation. Consider MLOps to be a success formula.

The challenge
The disparity between what AI/ML is used for at present and its potential usage, stems from a number of problems. These are largely related to model building, iteration, deployment, and monitoring. If AI/ML is to alter the global corporate landscape, these concerns must be solved. Organizations that have already begun their path to operationalize AI/ML or are generating Proofs of Concept (PoC) might avoid some of these pitfalls by proactively incorporating best-practices in MLOps to enable smooth model development and addressing scaling issues.

Worse still, organizations spend precious time and resources monitoring and retraining models. Successful machine learning experiments are difficult to duplicate, and data scientists lack access to the technical infrastructure required to develop.

Paving the way to implementation
The development of a Machine Learning model often begins with a business objective, which can be as simple as minimizing fraudulent transactions to less than 0.1 percent or even being able to recognize people’s faces in a photograph on social networking platforms. Additionally, business objectives can also include performance targets, technical infrastructure requirements, and financial constraints; all of which can be represented as key performance indicators, or KPIs, which further allow the business performance of ML models in production to be monitored.

MLOps help ML-based solutions get into production faster through automated model training and retraining processes, as well as continuous integration and continuous delivery strategies for delivering and upgrading Machine Learning pipelines.

MLOps practices and framework allow data engineers to design and build automated data pipelines, data ops platforms, and automated data feedback loops for model improvement, resolving more than 50 issues related to the lack of clean, regulated, governed, and monitored data needed to build production-ready models.

Way forward: The future of MLOps
According to our research, many organizations are keen to have centralized ML Operations in the future, as opposed to the current de-centralized approach. The benefit of this type of centralized learning is that the model can generalize based on data from a group of devices and thus work with other compatible devices immediately. Centralized learning also implies that data can explain all the differences between devices and their environments.

MLOps, while still an uncharted territory for many, is quickly becoming a necessity for businesses across industries, with the hope that it makes the business more dependable, scalable and efficient. If the benefits of AI are to be realized, the models that increasingly drive business decisions must follow suit. For years, AI has been optimized through DevOps in the way software is built, run, and maintained, and it is now time to do the same for Machine Learning. It is critical to make AI work at scale with MLOp.