Large Language Models and proven time-series analysis are set to redefine the functions of DevOps and SRE. They will play a significant role in ensuring that the software running in the cloud and modern infrastructure is always available.
, an ML model based on GPT-3. Codex can write programs in dozens of languages, including Python and Go. It’s been trained on 159 GB of Python code from 54 million GitHub repositories. With plug-ins for popular IDEs such as VS Code and Neovim, Codex empowers developers to automate most of their code.
Once the code is committed, AI reviews and analyzes to find blindspots in programs that may prove expensive.is a classic example of an AI-driven tool to analyze and profile code. It identifies critical issues and recommends ways to improve the quality of code. A modern CI/CD pipeline takes the code that passed all the tests and approvals and packages them into artifacts such as container images or JAR files. This step involves identifying the dependencies of the software and including them in the packaging. DevOps engineers are responsible for writing Dockerfile that defines the software's dependencies and the base image. This step is as crucial as software development. A mistake can prove to be expensive, leading to performance degradation.
While GPT-3-based models such as Codex, GitHub Copilot and chatGPT assist developers and operators, the same GPT-3 model can come to the rescue of the SREs. An LLM model trained on logs emitted by popular open source software can analyze and find anomalies that may lead to potential downtime. Combined with the observability stack, these models automate most of the actions a typical SRE performs.
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