Why MLOps is the way forward for organisations to fully leverage real-time data analysis
A panel of experts discussed the importance of implementing machine learning operations, or MLOps, within developer processes and how it helps make deployments easier.
Organisations looking to simplify workflow processes for implementing data strategies and product deployments can benefit greatly by following the MLOps, or machine learning operations approach, according to speakers at a panel discussion at YourStory's Mumbai edition of TechSparks 2023.
Aditya Mony, Group CTO, , said implementing MLOps has helped Servify leverage real-time data analysis to protect the company and customers from fraud.
“It could be situations where there’s a bad actor doing something wrong, and we don’t want to penalise genuine customers because of the bad actor. So it becomes incumbent on us to be able to understand what these issues are. And that real-time decision making can only come on the back of data,” he said.
’s CTO Deep Ganatra agreed with Aditya, saying that there’s always an immediate need to create personalisation on their platforms or risk losing that customer, something that MLOps has helped them with.
“Business teams always have the mindset that [a requirement request] will be handled by the tech team, and tech teams have the mindset that business teams won’t understand exactly how [our technology] works. But the reality is, each of those teams have their own requirements and challenges. Now we need to create a bridge between that; that's where MLOps comes in,” he explained.
On how organisations can adopt an MLOps strategy, Pramod Rajagopal, Head of Solutions Architecture, DNB and Commercial Businesses, India, Databricks, said three planning phases of implementation generally need to be followed: building reliable data pipelines that can scale with your business; the actual building of the model; and production positioning to run real time or near real time to get real value as quickly as possible.
“In general, people focus a lot of attention, time, and energy on the accuracy of the [MLOps] model itself. However, there is a huge pipeline of things that happen before you can even start looking at building a model. And then there are a lot of things that make it even harder to take that model into production. That entire journey is what an MLOps lifecycle looks like,” Pramod explained.
The panel also spoke about the need to cultivate an innovation mindset among employees to help scale businesses.
Deep said that the race to be first should never be a driving factor for innovating. “Innovations happen when your mind is calm. We need to focus on how to leverage the new things available in the market. I don't want to be first. I want to be innovative. You have to just keep that buzz aside and think practically how you’re going to solve a business problem.”
Pramod added that pacing yourself while innovating would add more value than being the first on the bandwagon. “One of the things I always tell my team is that it’s great to be passionate. But having passion about everything all at the same time is going to burn you out. So pick your top three [areas of focus] and change them every quarter. You have to pace yourself.”
