Readying the enterprise for AI-enabled DevOps
Many organizations are still in the early stages of digital transformation. They continue to work with legacy systems and have large amounts of historical data in silos. AI can help extract insights from such data for creating well-designed applications to enhance customer experiences. To realize these benefits, organizations should upskill and empower their existing DevOps and data science personnel.
Data science teams may need to be educated about the benefits of adopting strategic DevOps practices like version control for development, model lineage tracking, model training and testing frameworks, etc. These practices can improve incremental feature delivery and enhance personalization by identifying user-specific patterns in application usage and tailoring features accordingly. In addition, DevOps engineers should work closely with data scientists and ML engineers to accelerate response time and efficiently track and manage all aspects of model development and production.
Close collaboration can also help ML engineers initiate model retraining and manage model versions using CI/CD and containerized applications, as part of MLOps. While this is an ambitious undertaking, it can help improve key metrics across the DevOps lifecycle – from idle time to mean time to repair (MTTR) to release frequency. Achieving desired metrics through AI-enabled workflows that continuously learn and improve performance can help enterprises produce world-class applications while saving cost.
Since orchestration and monitoring form the backbone of DevOps, AI offers myriad opportunities to automate operations and deliver real-time insights for improving product development and releases with quality and efficiency. As both AI and DevOps become more mainstream, enterprises will increasingly break organizational silos and adopt new automation-led tools and strategies to improve business outcomes.
Reprinted with permission from Datanami