The Ultimate Guide To Understanding DevOps, MLOps, and DataOps: Key Similarities and Differences

Unlock the secrets of DevOps, MLOps, and DataOps to streamline your data-driven projects!

2 min read
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Looking back, I realized I was doing DataOps before the term existed.

Coming from the software development world made it natural for me to take devops practices and culture and apply it to fill in the gaps I was seeing in our rapidly growing data team.

It can be very confusing to hear all these terms thrown around.

Let me help break it down.

The promise of Dev/ML/Data OPs practices are the same:

Increased agility; Faster Innovation; Delivery of higher ROI products; Consistency of governance & security

DevOps came first; Foundation for the other two.

It is the way successful organizations are industrializing the delivery of quality software today

The best way to understand Devops is by looking at a lifecycle diagram. Key components of devops are:

  • Version control system for the collaborative development process

  • Continuous integration and delivery for rapid delivery of quality software

  • Monitoring and alert for continuous feedback from production

  • Infrastructure as code to automate repetitive infrastructure work

If the above tools and ideas are new to you, I propose you study and adopt devops culture before you dive too deep into DataOps or MLOps.

Now, let us look at the specialized versions for data teams:

MLOPs is DevOps + Ops for building and productizing ML

MLOps practices are not as mature as DevOps but the objective is the same.

On top of the above components for code, MLOps introduces some additional pieces:

  • Experimentation and changing requirements for the input data

  • Retraining pipeline to keep the model current

  • Model serving deployment alongside the software product using it

  • Model performance and drift detection

DataOps is DevOps + Ops for building and productizing data assets

DataOps also builds on top of the DevOps fundamentals. So first, build a strong DevOps culture in your team.

Additional components in DataOps to deliver data products are:

  • Data Tests to enable continuous deployment of trustable data

  • Data lineage and end to end orchestration for faster incident resolution

  • Anomaly detection to detect data drift proactively

  • Data catalog to increase collaboration with business users

  • Data governance to have consistent security practices

Once you better understand the different value propositions and components of Dev/ML/Data Ops. You are in a position to leverage the right approach for the right problem.