How DevOps Can Boost the Value of DataOps and MLOps

Explore how to scale your enterprise data management and analytics initiatives with the best DataOps, MLOps, and DevOps practices

Enterprise deployment of intelligent applications increasingly rests on a three-legged stool of data, AI/ML models, and program code builds. DevOps is an increasingly popular enterprise practice focused on continuous integration, testing, and deployment of application releases. Adoption of DevOps tools enables IT operations to improve application quality and more effectively automate and accelerate the coordinated orchestration of data, ML, and program code into production deployment of analytics-infused applications. 

Recent TDWI research shows that enterprises are beginning to unify DataOps and MLOps pipelines in their operation and that they are also beginning to align these pipelines with DevOps practices. Join this webinar in which Impetus and TDWI experts discuss issues surrounding the convergence of DataOps, MLOps, and DevOps practices in enterprise data management and analytics initiatives. Key questions that they will explore include:

  • What exactly is DevOps?
  • How can DevOps principles—focused on continuous integration, testing, and deployment of applications—add value to DataOps and MLOps as enterprise practices? 
  • To what extent are enterprises already adopting—or at the very least, exploring—DevOps in their DataOps and MLOps pipelines? 
  • What investments in tools, platforms, and professional skills enhancement do enterprises need to make to align their DataOps and MLOps practices with DevOps? 
  • What changes to data analytics application development practices and skill sets do enterprises need to make in order to more fully comply or align with DevOps? 
  • What’s the downside in added complexity, cost, and risk associated with bringing DataOps and MLOps into alignment or conformance with DevOps? 


Chandrasekar Natarajan,

Senior Manager, Software Engineering 
Impetus Technologies

James Kobielus,

Senior Research Director, Data Management

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