26 Oct 2021
Over last few years, organizations have been busy experimenting with ML models for specific use cases and working with data scientists to optimize model accuracy/ performance. Presently they want to deploy these models at scale, but there are a good deal of new challenges ahead of them!
In this episode, Infosys AI experts Amit Gaonkar and Kaushal Desai tell us why organizations need to think of ML Ops in strategic way – not just as a toolkit to automate deployment of machine learning models. Listen to know how a good Enterprise ML Ops layer, built on good architecture principles – enables organizations to build future proof, scalable & responsible Enterprise AI with adaptable governance mechanisms.
Hosted by Abhiram Mahajani, Sales Director, AI and Automation Services, UK and Europe, Infosys
“It (Machine Learning model) has to be deployed in a manner that it is completely scalable"
- Amit Gaonkar
“The choices are a lot, these are evolving quite quickly. And that's why we always recommend not to stick with a partner or a vendor, but try and adopt an enterprise architecture which, which is flexible, and you can mix and marry a lot of these things.”
- Kaushal Desai
“So we have actually two things that we want to do within Infosys. To take these offerings to customers, our workforce needs to be enabled on AI. And to that extent, we want to democratize AI as much as possible within Infosys. And the second challenge is, we want to harness these machine learning models, which are specific to a particular problem statement.”
- Amit Gaonkar
What is The Applied AI Podcast?
Abhiram introduces Amit and Kaushal
Let's start with just the pure definition of it. So, what does MLOps mean in layman's term, Amit if I may ask you?
Why is this topic so relevant right now, like what has changed over the past few years for MLOps to suddenly get into the limelight?
What are the different stages of this lifecycle? And you know, how exactly is our organization going through this lifecycle today? What sort of tools skills are being an put at play here?
For example, in layman terms, I see data science is a skill set, but the skills that are required to do this span much beyond just data sciences, is what I could understand. So, could you talk a little about that? Or am I wrong? Is it the same skill being extrapolated?
I think you did mention a telco customer So can you give a specific example?
How exactly is Infosys, we are addressing this space? If you could just do a quick summary of that?
There's also this emerging space of ethical AI or responsibility AI? Does that have any inter linkage with this topic? Or These are two separate conversations in themselves?
Abhiram shares how to connect with Infosys applied AI