Resolved: What is DataOps? Where does it makes sense and how does it fit...

Hi Manesh,

Good to hear from you!

Do we talk about Data Ops somewhere in the course?

This position/role sounds a bit odd to me as I have not heard about it so far. We can have ML Ops. This is a role which is at the interesection of computer science and data science. An ML Ops Engineer is responsible for the productionization of ML models. This means implementing ML models in a live production environment, which is a rather sophisticated task and requires significant skills.

Here is a useful YouTube video of ours in which we describe the different roles in data science:

https://www.youtube.com/watch?v=CIdSwQqRKc4&t=104s

Best,

Ned

Hi Mahesh,

As you may have found out in your initial research, "DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics."  (source: https://en.wikipedia.org/wiki/DataOps).

This means that it becomes particularly relevant when data professionals need to perform rapid, specific data pipeline deployments and modifications. This allows them to reducing manual and time-consuming processes, and focus on other tasks such as analysis or modelling. By eliminating the need to wait for data to finish operations, data teams and data users increase their productivity.

My data literacy course is for "beginners", which is why we are not covering it here. However, if you continue your learning journey to become a true data professional, it is very likely that you will be exposed to DataOps.

I hope this helps,

Olivier