Building a Scalable MLOps Stack — From Data to Deployment
This article explores the development of a streamlined ML system, showcasing the journey from data to deployment, simplifying complex processes for effective machine learning operations.
In today’s data-driven landscape, machine learning has transformed how we harness insights from vast datasets. However, transitioning from raw data to a deployable machine learning model poses various challenges, including data prep, model training, and deployment. This is where MLOps comes into play, merging DevOps best practices with data science requirements to ensure models not only get built but also thrive in practical applications.
MLOps, far from just a buzzword, represents a set of practices and tools bridging the gap between data science and operationalization. A well-structured MLOps stack empowers you to automate these processes, turning data science experiments into reliable pipelines that deliver tangible real-world value.
What is the project about ?
The core concept driving this project is the development of an all-encompassing MLOps stack, using a simple book recommender system as a…
