Data Engineering & MLOps

Data Engineering on a broad level deals with the data infrastructure (pipelines, warehouses etc) and MLOps is majorly concerned with the whole development and deployment cycle of an AI or ML model.

What is Data Engineering?

Data Engineers are essentially like utility workers. They collect, generate, store, and process data on a consistent basis across a variety of systems.

Being responsible for the ongoing maintenance of all data retrieval/storage systems/infrastructure, collecting and providing support for other teams who require access to this information is key to the success of their operations and success as an organization that provides relevant services or products to consumers.

Why is Data Engineering Important?

Data Engineers are crucial to the digital transformation of any business. Inside every company, Data Engineers build the infrastructure that allows data scientists to work at their best.

Their work includes building reliable and secure cloud solutions for organizations so that their data can be examined and manipulated with ease.

Companies need in-house staff that can make sense of the huge amounts of data needed to become an AI-driven business to be competitive worldwide.

What is MLOps?

It is a hybrid of machine learning (ML) creation and deployment. It belongs to the field of unifying ML systems development and ML systems deployment, to streamline and standardize the continuous delivery of high-performing models in production.

Why MLOps?

A few years ago, the amount of data we were handling was manageable; there weren’t that many models, and our scale was small. This is turning out to be very different now, we are integrating decision automation into a wide range of applications. ML-based systems can create technical challenges when they come to the building and deploying things.

Now, the development and deployment of ML models involve several different teams of a data-driven organization like product team, data engineering, data science, and IT/DevOps.

By implementing this new machine learning engineering culture (MLOps), we’ve been able to streamline the entire workflow and create better working relationships between data scientists and dev ops in order to develop and deploy systems faster.