Why AI Engineering Is Becoming More Like Distributed Systems Engineering
📰 Dev.to AI
AI engineering is becoming more like distributed systems engineering due to the complexity of production AI workflows
Action Steps
- Build a production AI workflow using a distributed systems approach
- Configure orchestration and retries for reliable model execution
- Implement queues and workflow state management for efficient model deployment
- Apply observability and evaluation techniques to monitor model performance
- Scale AI workflows using distributed systems principles
Who Needs to Know This
AI engineers and distributed systems engineers can benefit from understanding the similarities between the two fields to improve production AI workflows
Key Insight
💡 Production AI workflows require a distributed systems approach to manage complexity
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🤖 AI engineering is becoming more like distributed systems engineering! 🚀
Key Takeaways
AI engineering is becoming more like distributed systems engineering due to the complexity of production AI workflows
Full Article
As foundation models continue to improve, I think AI engineering is starting to look far more like distributed systems engineering. The difficult part usually is not the model itself - it is everything around it: Orchestration Retries Queues Workflow state Observability Evaluation Scaling A production AI workflow can very quickly become: Retrieval Multiple LLM/tool calls Asyn
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