As the new year begins, I can’t believe it’s been two years since I graduated and started working as a Cloud Engineer at Thoucentric. These past few years have been filled with so much learning and growth, both personally and professionally. I bought this domain a few years ago with the intention of using it to reflect on my work, but I’ve realized that I haven’t been doing much reflecting at all - the fact that I only have two blog posts published being a proof of that. I’ve been busy with work and haven’t made the time to write about it. But today, I want to change that. I’m not usually one for New Year’s resolutions, but something inside me just clicked and I feel like now is the time to make a change.

Now, what exactly clicked? Before that, let me ask you one question: Have you ever had one of those moments where you just can’t stop thinking about something because it’s just so mind-blowing? That’s exactly what happened to me when I came across ChatGPT. It was like nothing I’d ever seen before, and it felt like the pinnacle of human ingenuity was right at my fingertips. But it’s not just what ChatGPT did that had me excited - I’ve been working with machine learning models for a while now and have seen firsthand the challenges that organizations face when trying to bring them into production at scale. So how did ChatGPT manage to make it all look so easy? I wanted to learn more about how it worked and how I could apply that knowledge to my own work at Thoucentric, we have been been ramping up our Machine Learning Operations (MLOps) capabilities and the working of ChatGPT was a fascinating case study to take inspiration from.

And with all of this in mind, it clicked. “I should be learning and writing more about MLOps” is what I told myself. And that’s all that I’ll be doing going forward. For those who may not be familiar with MLOps, it is a set of practices and tools that enable organizations to streamline the development and deployment of machine learning models.

Over the coming weeks and months, I’ll be sharing my experiences and insights as I work to ramp up MLOps capabilities in our organization. I’ll be covering topics such as setting up a continuous integration and delivery (CI/CD) pipelines, implementing version control for machine learning models, deploying models to the cloud at scale and monitoring and debugging models in production.

I’m looking forward to sharing this journey with all of you, and I hope that my experiences and insights will be helpful to those of you who are also working to explore and build MLOps capabilities in your own organizations.

Stay tuned for more updates and be sure to follow along as I share my experiences and insights along the way.