Oops! You might lack these Ops

Why just DevOps is no longer enough

  • A larger number of participants in the development process. DevOps removed silos between development and operations. However, in most organizations, there are many other silos, such as product owner-development, business development-sales, data management-development, etc.
  • The emergence of big data. While DevOps focuses on the rapid and continuous delivery of working software, they do not work on data orchestration, updating, and secure access to it.
  • Growing usage of Git. Traditional DevOps CI/CD pipeline workflow is gradually replaced with Git operations. For example, the teams may commit their changes to Git and then directly deploy a program using Kubernetes.
  • Rise of machine learning. Traditional DevOps instruments may not cover the needs of ML applications and workflows. However, special frameworks like MLOps AWS or Microsoft address the full lifecycle of the machine learning models, including development, testing, deployment, operation, and monitoring.
  • Variety of IT infrastructure tools. This requires choosing the most effective IT instruments from the price/quality perspective. While DevOps is well-versed in the technical aspects, FinOps may better handle the money-planning responsibilities.

First DevOps siblings: DataOps and GitOps

  • standardize and reuse core data pipeline components: ingest, transform, clean, etc.
  • create data science sandboxes on demand
  • deploy models automatically
  • create app logic and data permissions that promote easy but secure data access
  • assign agile teams to business groups to create more efficient programs

ML Ops, AI Ops, FinOps, NoOps, and other jobs aka Ops

MLOps

  • chooses technologies to develop and train ML models
  • adjusts the environment to run ML models
  • simplifies communication between data scientists and data engineers
  • manages model risk
  • configures the model monitoring system

AIOps

  • full automation of routine tasks
  • fast detection of security issues
  • close interaction between data center groups and teams
AI Ops examples

FinOps

  • technology analysis
  • trends studying
  • cost forecasting
  • benchmarking
  • variance analysis

NoOps

Which Ops suits your project best?

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