Beginning a thread on the ML engineer starter pack (please contribute):
- ”example spark config” stackoverflow post
- sklearn documentation
- hatred for Airflow DAGs
- awareness of k8s and containers but no idea how to actually use them
- “the illustrated transformer” blog post
- ”example spark config” stackoverflow post
- sklearn documentation
- hatred for Airflow DAGs
- awareness of k8s and containers but no idea how to actually use them
- “the illustrated transformer” blog post
- silent numpy broadcasting errors
- cursing US-West-2 for not having any instances available
- reviewing data scientists’ code & wishing it was cleaner
- reviewing software engineers’ code & wishing your code could be half as good as theirs
- cursing US-West-2 for not having any instances available
- reviewing data scientists’ code & wishing it was cleaner
- reviewing software engineers’ code & wishing your code could be half as good as theirs
- battered copy of Martin Kleppman’s “Designing Data-Intensive Applications”
- weekly emails from ML tooling startups trying to sell their products
- spending 10x time cleaning data as training models on the data
- weekly emails from ML tooling startups trying to sell their products
- spending 10x time cleaning data as training models on the data
- git commit -m “Minor fixes.”
- model.save(“./checkpoints/final_model_v14)
- thinks “i need another intern” multiple times a day
- can’t reproduce the data scientist’s results but productionizes the model anyways
- googles “how does t-sne work” every few months
- model.save(“./checkpoints/final_model_v14)
- thinks “i need another intern” multiple times a day
- can’t reproduce the data scientist’s results but productionizes the model anyways
- googles “how does t-sne work” every few months
