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datascience's Issues
Add a Poetry project to our repository using Python 3.7 and include newest version of Pandas
Deploy HelloWorld app as a Cloud Function
Add a containerized HelloWorld app using FastAPI and test it locally
Structure the repo without task numbers
Create a ZenML pipeline downloading data from BigQuery in one step in local stack
Setup a ZenML pipeline using Feast to get training data
Come up with event proposals that could get funded by DDSA
See open call here: https://ddsa.dk/open-calls/open-call-funding-of-data-science-events-courses/
Here is one idea: Maybe we could invite ZenML to speak and have people submit Kaggle notebooks (maybe as a competition) with examples also presented at the event.
Please come up with three more ideas.
Complete the qwiklab on Vertex AI https://www.qwiklabs.com/focuses/18940?parent=catalog
Learn IaC in practice by reading and completing this Terraform qwiklab: https://www.cloudskillsboost.google/quests/159
Add pytest code coverage to GitHub action
Describe the steps to deploy a Python package using poetry to GCP so it's pip installable (use artifact store?)
Sum up why BentoML is useful and it's alternatives
Add ZenML to the project using poetry
Add a GitHub action that runs Pytests on PRs in the project
Briefly sum up in one slide how Poetry is a different compared to PipEnv
Describe best practice logging in Python and make sure it works with Cloud Functions, i.e. make an API call that logs a message in GCP. The log should be implemented as a decorator.
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