Stop Experimenting with AI and Machine Learning

The ability to make fast, data-driven decisions has never been more valuable as businesses grapple with the shift toward hyper-personalisation, driven by rapidly changing customer behaviours and expectations.

The pandemic has accelerated the imperative for businesses to invest in Artificial Intelligence (AI) and Machine Learning (ML) so they can replace guesswork with data-powered certainty to reorient strategy and optimize operations for success in an uncertain future.

Nevertheless, enterprises often struggle to integrate these technologies at scale and monetize the benefits. Stumbling blocks typically include challenges associated with cost, lack of investment protection, undefined business outcomes, lengthy timeframes from development to deployment, lack of expertise, and the complexities of the regulatory landscape.

Gartner predicts that by 2022, at least 50% of ML projects will not be fully deployed into production.

The emergence of MLOps is a game changer

Machine Learning Ops, or MLOps, is a set of best practices that enables companies to generate value from AI/ML much faster. It promotes collaboration between data scientists and operations professionals and streamlines the building, testing, deployment, and governance of AI/ML models. MLOps is a crucial ingredient in the recipe for success for any modern business seeking to implement and derive value from AI/ML at scale.

It doesn’t matter where you are on your cloud journey

There’s no one size fits all. MLOps implementation should be governed by business priorities and current levels of AI/ML adoption. That said, establishing a robust MLOps framework is fundamental to streamlining the process from model creation through training to deployment so companies can reap value from AI/ML much faster.

For those considering embarking upon an AI/ML journey, but with plans to scale rapidly, it makes sense to use entire cloud-based suites for both model development and MLOps.

Businesses already well into their AI/ML roadmaps need flexible options for multi-cloud use cases and to leverage technology-agnostic solutions to further optimize their margins and costs.

Those with significant AI/ML models and assets like data, insights, and reports can jumpstart MLOps on cloud or on-premises infrastructure with a framework that allows the preservation and movement of pre-existing models through the different stages of training to deployment and the monitoring of successes and failures.

How can you harness MLOps to optimize your AI/ML investment?

In a recent study, Forrester found that 98% of IT leaders believe that MLOps will give their company a competitive edge and increased profitability Yet only 6% of ML practitioners have mature MLOps capabilities.

Cognizant helps businesses harness MLOps and create value wherever they are on their AI/ML journey.

Cognizant’s proven AI/ML framework optimizes the costs of implementation with a structured approach, concise timelines, and deep experience demonstrating feasibility. In partnership with AWS, the process is faster; our combined capabilities fast-track sustained ROI.

Leading financial service provider realizes $60 million in cost savings with AI/ML

Cognizant achieved a $60 million reduction in fraud-related costs for a leading financial services provider with an MLOps solution that fast-tracked the deployment of an analytics platform to enable real-time decision-making on credit-card transactions.

Our MLOps solution, which encompassed AutoML pipelines, real-time Model Monitoring, Model Versioning and Calibration, resolved production delays and enabled the industrial deployment of AI/ML models designed to analyze changing fraud patterns without increasing false positives.

To learn more, go here.