Towards Data Science
I Replaced GPT-4 with a Local SLM and My CI/CD Pipeline Stopped Failing
β’1 min readβ’
#llm#deployment#rag#compute
Level:Intermediate
For:ML Engineers, Data Scientists, AI Product Managers
β¦TL;DR
Replacing GPT-4 with a local Self-Supervised Learning Model (SLM) resolved issues with a Continuous Integration/Continuous Deployment (CI/CD) pipeline, highlighting the challenges of probabilistic outputs in reliability-critical systems. This switch from a cloud-based model to a local one improved the pipeline's stability, underscoring the importance of considering output determinism in system design.
β‘ Key Takeaways
- Probabilistic outputs from models like GPT-4 can introduce unpredictability and instability in CI/CD pipelines.
- Local SLMs can offer more control over model behavior and outputs, potentially reducing pipeline failures.
- The choice between cloud-based models and local models should consider the need for reliability and determinism in system outputs.
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