MLOps best practices for digital transformation

Looking to leverage MLOps in your digital transformation initiative? Focus on people, data processes, and technology
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Across every industry, organizations are investing in artificial intelligence (AI) and machine learning (ML) to unlock business insights and make data-driven decisions. However, in our experience working with enterprises across multiple industries, we’ve seen that only half of all AI proof of concepts ever scale to production. Machine learning operations (MLOps) can help many enterprises substantially increase that success rate.

At its core, MLOps helps organizations consistently develop, deploy, monitor, and scale AI and ML models. It’s a framework for sustainable innovation and a process for scaling AI in enterprises, reducing costs, boosting efficiency, generating actionable insights, and creating new revenue opportunities.

We conducted a study in partnership with the MIT Sloan CIO Symposium, and CIOs consider AI and ML the top technologies that will help them achieve their business goals.

[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]

For AI and business leaders to reap the benefits of leveraging MLOps best practices as part of a digital transformation, consider focusing on three areas: people, data processes, and technology.

Build an ML team with a diverse skillset

While there is no one way to build an ML team or a team for HR or marketing, it is essential to foster an environment of collaboration and communication. And keep in mind that undergoing an AI-led digital transformation is a process that requires more than just talent. There’s also organization-wide change management.

Undergoing an AI-led digital transformation requires more than just talent. There’s also organization-wide change management.

For companies accelerating their AI transformation, it is critical to build a team of experts across various disciplines. For example, a team may include subject matter experts, data scientists, ML engineers, ML architects, etc.

With technology evolving daily and many organizations implementing innovative ideas, it’s incumbent upon AI leaders to tap into the creativity and power of individuals with diverse skill sets. For example, consider hiring those with experience in ML architecture design, cloud ML engineering, and DevOps, to name a few.

Create a cohesive data storage and business process system

Once you have built a talented team to manage your company’s AI and ML assets, examine how you organize corporate data. Remember, companies that have been around for many years may have legacy systems, which might take time to restructure. Examine how employees access data – is it easy or difficult to find existing information? Is the security at an appropriate level? Are governance policies in place, or do they need to be updated?

Consider this example: a global reinsurer must compete more effectively in the life insurance market with faster, automated underwriting and claims management to offer routine coverage at the point of sale, harness new data sources, and be more customer friendly. The reinsurer studied its end-to-end system, including the infrastructure, organizational maturity, and existing IT governance and controls. They designed an architecture evolution roadmap navigating the stages of standalone to distributed to cloud-ready, and finally, continuous delivery for the predictive analytics model. The ML model deployed takes only 14 seconds to furnish results for 200 entries, resulting in high underwriter efficiency and a potential 12-15% annual top-line increase.

Remember, organizing data isn’t just about increasing efficiency. For highly regulated industries such as healthcare and financial services, ensuring security and compliance and eliminating data biases are particularly important.


ML platform and tool decisions are critical to achieving the planned ROI from MLOps initiatives. Identification of the tool must be in line with the organization’s overall cloud and data strategy. Organizations can decide between open source libraries and tools to define their MLOps stack or leverage cloud or on-premises MLOps platforms.

You should take a phased approach to retiring legacy systems and platforms and adopt best-in-class MLOps systems that enable various stakeholders to collaborate with minimal conflicting objectives.

Tapping into the power of MLOps and AI equips company leaders with the vital information they need to make data-driven decisions that turn insights into action. In a world of continually changing business and economic environments, that type of support can help a company drive better performance and seize new opportunities.

[ Get answers to key digital transformation questions and lessons from top CIOs: Download our digital transformation cheat sheet. ]

Amaresh Tripathy is the Business Leader of Analytics at Genpact, a global professional services firm delivering outcomes that transform clients' businesses. He leads an extensive global team of analytics experts covering data engineering, data insights, AI and machine learning.