IBM/data-science-best-practices
The goal of this repository is to enable data scientists and ML engineers to develop data science use cases and making it ready for production use. This means focusing on the versioning, scalability, monitoring and engineering of the solution.
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Stargazers
- aimlnerdNetherlands
- alihashmi-whIBM Consulting
- BenchengWCIBC
- bridgerhuang
- BryanJianChristchurch, New Zealand
- coathanger3
- DantasBFounding engineer at @findly-ai
- geodyangCalgary, AB
- gpadillax
- guim4devCo-Founder @Hemocione & Software Enginner @layers.education
- henchavesGiskard
- ibethrudden
- jonathanGGa
- Kablys
- kemorick
- LittleWei23
- maxshowarth
- nbk905
- nguyenduythanghn2004
- oezguensiIBM
- osaliu
- pankajk156
- Piyushi-0IIT Hyderabad
- pjaselin@VulcanForms
- RafaelxFernandes@INGEN-S-r-l
- sebastian-hirschl
- shayandavoodiiResearch Assistant @ AUT
- SindhuVarier
- stephenpcookUniversity of Exeter
- sturtison@ibm.com
- tgalulaIsrael
- thomas-pfeiffer@IBM
- tkhk11
- upkarlidderSan Francisco
- wouteroosterbosch@IBM
- wpsmithtwcIBM, The Weather Company