Demystifying the “Ops” Family: DataOps, ModelOps, and DevOps

akash-jattan
Akash Jattan
|
29 Feb 2024

We recently touched on the “Ops” family – DataOps, ModelOps, and DevOps – but we know that you are still seeking clarity on their differences and how they collaborate. So, let’s dive deeper into this:

DataOps: The Data Engineering Maestro

  • What: A methodology focused on communication, collaboration, and automation for efficient data pipelines. Think of it as CI/CD for data, ensuring clean, reliable data for analysis and modelling.
  • Who: Owned by Data Engineering or the Data Team.
  • Users: Data Engineers, Analysts, BI Developers, Data Scientists.
  • Capabilities: Release management, data products, team-based development, version control, continuous integration/delivery, test-driven development.

ModelOps: The Model Guardian

  • What: A methodology focused on tools, technologies, and best practices to deploy, monitor and manage machine learning models that help builds organisations capability for scaling and governing AI at enterprise level.
  • Who: Owned by a dedicated Model Operations Team.
  • Users: Data Scientists, CloudOps/DevOps, Data Engineers, MLOps Engineers, SREs.
  • Capabilities: Enterprise model inventory, data/model products, complete model lifecycle automation, 360° visibility/monitoring/auditability, rapid ML deployment, data science performance monitoring, decision optimisation, and transformational models.

DevOps/SRE: The Infrastructure Architect

  • What: A set of practices fostering collaboration and automation between software development and IT operations. It streamlines infrastructure management and deployment.
  • Who: Owned by a DevOps Team.
  • Users: CloudOps, SREs.
  • Capabilities: Continuous integration/delivery, infrastructure as code, automation.

Beyond the Differences: A Symphony of Collaboration

While distinct, these “Ops” are not isolated entities. They work together harmoniously:

  • DataOps provides the clean data foundation for effective models.
  • ModelOps ensures models perform optimally in production.
  • DevOps underpins both with robust infrastructure and automation.

By understanding and implementing these “Ops” methodologies, organisations can create a more efficient and effective data-driven culture, leading to better decision-making and improved business outcomes.

Want to learn more? Share your thoughts and experiences in the comments!

Demystifying the “Ops” Family: DataOps, ModelOps, and DevOps
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akash-jattan

Akash is a senior leader experienced in creating emerging products and leading data transformations programs. His expertise is business transformation by leveraging modern data and AI solutions. He has worked in Australia and NZ across multiple industries successfully launching multiple products to market and leading multiple data-driven business transformations programs. Akash has consistently demonstrated strong leadership in building executable visions, creating high performing teams and commercialising products.

It was his passion to drive business transformation through data, that drove him to become the Founder and now CEO of dataengine in 2018. Akash regularly speaks at universities and conferences about the evolution to DataOps as a foundation to analytics in the business.

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