Machine-learning operations can help you capitalize on your data strategy and derive deeper business value.
Aug. 18, 2021 | By Ramesh Vishwanathan
Organizations are pushing the boundaries of how much more value they can unlock from their data—and they are using machine-learning operations, or MLOps, to do it. Leveraging MLOps creates value, generates new revenue streams and improves customer experiences.
But what exactly is MLOps? Conceptually speaking, it’s very similar to DevOps. Both lend themselves to increasing the velocity of the machine-learning development process and improving operations. Machine-learning (ML) application simulates real-world behaviors and patterns based on your dataset, so the data itself becomes a critical part of modeling life cycle management with automation helping to drive agility and accuracy. While DevOps points toward a single dimension of managing applications and code, MLOps takes it a step further—not only managing code but the data along with the code.
Introducing MLOps to your organization
For MLOps to be effective, organizations need to standardize processes across the life cycle of model development, testing, deployment and management. ML and automation start by harmonizing organizational data so that all stakeholders are using the same data structure. Centralizing your data and having feature stores will help you evolve your ML models to best fit your organization and increase velocity through reusability—whether it’s on the cloud, on-prem or a mix of both. The direction you choose to go in will depend on how mature your organization is in its ML journey, as well as your organization’s appetite for ML.
MLOps is typically an organization-wide initiative that requires strategic processes, tools and governance to maximize value. Beyond optimizing your data strategy, cultural changes and leadership buy-in are critical to adopting an MLOps culture.
The benefits of MLOps for an organization and their data strategy
When it comes to business value, the question organizations typically ask is about time to value: how can I optimize and operationalize this process to achieve more? In terms of MLOps, it means figuring out how to speed up the life cycle of taking a model from lab to production and derive business value to inform insights and intelligent automation recommendations—translating data into actionable decisions and meaningful business outcomes.
Once you define and streamline your internal processes, data-driven intelligence can be embedded into the process automation life cycle. That’s where MLOps can shine. Embedding MLOps in a true outcome-driven process will remove the manual process out of your systems to help you move ML models across your applications through testing, training and go-live. This will support the continuous improvement life cycle to combat model degradation. As your organization matures its machine learning, your process and automation workflows will reap the benefits from concept to realization of ML deployments.
The challenge organizations face when adopting MLOps
Organizations are using technologies like machine learning and AI, but they seem to struggle with creating value. The biggest challenge in the ML world is around operationalization and time to market. MLOps aims to solve this problem by bringing in automaton through the life cycle of ML application development. The key aspect to realize as organizations embark on an MLOps journey is the breadth of systems and tools (i.e., applications, infrastructure, automation tools) process (i.e., development, testing, deployment, monitoring, automation, deployment and management) and people (i.e., data science, application development, data engineering, infrastructure and deployment).
For MLOps to be successful, all the of the above facets must work in harmony, but one of the biggest challenges is being able to get all the teams together to establish and agree to standardized tooling and processes. Ideally, MLOps must be looked at as a journey where organizations mature iteratively—evolving from a state where manual work is augmented with automation, to a state where a majority of activities are automated with minimum manual intervention.
About the Author
Ramesh is a data insights leader, innovator and AI evangelist. He leads TEKsystems’ data insights practice, focusing on cloud, big data and AI strategy. He is an expert at helping customers adopt and mature AI and helps lead them to data-driven automation and intelligence-driven business processes.
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