How DevOps Practices will Expedite AI Adoption in 2022? — Techment

  • The integration of product development into IT operations (commonly referred to as DevOps)
  • The focus is on continuous delivery. Both focus on automation, continuous monitoring, and sharing of information and processes throughout product development and IT operations.
  • Speed-Up Process: For many companies, AI development is still new and for such a new operation, a testing environment needs to be created. A time-consuming process of deploying code to software and then testing becomes cumbersome. DevOps eliminates such time-consuming tasks, hence faster time-to-market.
  • Enhances Quality: AI heavily depends on the quality of data processed by them. Training AI models on bad data lead to skewed responses resulting in a bad outcome. When unstructured data appears in the AI development process, the DevOps process helps in cleaning datasets and improves model quality.
  • Scales AI: As AI has too many roles and processes, scaling it is a challenge. DevOps unburdens the AI with faster delivery and eliminates rework and allows team members to focus on the next step.
  • Brings Stability in AI: Continuous integration is one process in DevOps that prevents the release of products in case any fault appears. Hence, support the release of an error-free model, which is more reliable and stable.
  • As AI/ ML is based on experiments and iteration of models, it takes ample time to build, train and test the model. So carve out a separate workflow and accommodate different timelines for building and testing.
  • This isn’t a one-time construction model but the consistent improving model that can deliver value without compromising. So collaborating with the team to consistently improve the practice, error check will improve the model lifecycle and its evolution.
  • Maintaining traceability,
  • Recording experiments,
  • Searchability of models,
  • Visualizing model performances, etc.
  • DevOps team needs to monitor the system for health checks.
  • Data scientists need to monitor model degradation, testing, etc and collaborate with the DevOps team.
  • Conduct continuous feedback from data scientists.
  • Identify and conduct training goals for each role in AI application.
  • Set training goals for data scientists, DevOps teams, and IT leaders and make sure access to tools and resources are available to teams.



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