Greensteam subscribed to the idea of doing #MLOps at a reasonable scale.
Seeing the quickly growing number of customers (= ML experiments), they decided to build their MLOps stack from 0 and solve all core problems around it.
Here are some of the issues → solutions:
Seeing the quickly growing number of customers (= ML experiments), they decided to build their MLOps stack from 0 and solve all core problems around it.
Here are some of the issues → solutions:
- 1000s of Jupyter notebooks → git
- Managing dependencies and reproducibility → @Docker
- Dealing with unit tests (in some parts of the model code) that don’t test → running smoke tests
- Managing dependencies and reproducibility → @Docker
- Dealing with unit tests (in some parts of the model code) that don’t test → running smoke tests
- Different linter versions showing different results locally and in Jenkins → code checks moved into Docker
- Finding parts of the code that unit tests didn’t cover → mypy
- Testing models for multiple datasets of different clients in different scenarios → @argoproj
- Finding parts of the code that unit tests didn’t cover → mypy
- Testing models for multiple datasets of different clients in different scenarios → @argoproj
- Monitoring the results of models, trained on multiple datasets, with different parameters and metrics (and comparing all those model versions) → neptune.ai
- Training a separate model for each vessel type with constantly growing time-series datasets → @FastAPI
- Training a separate model for each vessel type with constantly growing time-series datasets → @FastAPI
Full MLOps tool stack: buff.ly/3UnBr0i
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