How To Organize Continuous Delivery of ML/AI Systems: a 10-Stage Maturity Model | Outerbounds
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How To Organize Continuous Delivery of ML/AI Systems: a 10-Stage Maturity Model | Outerbounds
Top considerations when choosing foundation models
Accuracy
Cost
Latency
Privacy
Top challenges when deploying production AI
Serving cost
Evaluation
Infra reliability
Model quality
Accuracy
Cost
Latency
Privacy
Top challenges when deploying production AI
Serving cost
Evaluation
Infra reliability
Model quality
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End to end ML Project
Project setup:
Model training:
Run: python main.py
Model... See more
Project setup:
- Open this in VSCode
- Install Dev Containers
- Do Cmd + Shift + P -> Dev Containers: Rebuild Container Without Cache
- Activate the conda virtual environment: source activate endtoend
- Inside Dev Container, run mlflow and prefect local servers: nohup bash ./start_backend.sh
Model training:
Run: python main.py
Model... See more
arghhjayy • GitHub - arghhjayy/EndToEndML: End to end ML pipeline written with open source tools exclusively
No amount of AI will automate away corporate dysfunction.
However, we should not be optimizing for the rate at which we declare things “done” in isolation on a branch. We should optimize for the overall lead time — the time it takes us to deliver valuable software to users. Optimizing for “dev complete” time is precisely what causes “integration hell.” A painful and unpredictable “last mile” of integratio
... See more