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🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞

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[![Mend Renovate](https://app.renovatebot.com/images/banner.svg)](https://renovatebot.com) This PR contains the following updates: | Package | Change | Age | Adoption | Passing | Confidence | |---|---|---|---|---|---| | [protobuf](https://developers.google.com/protocol-buffers/) | `==3.20.1` -> `==4.24.3` | [![age](https://developer.mend.io/api/mc/badges/age/pypi/protobuf/4.24.3?slim=true)](https://docs.renovatebot.com/merge-confidence/)...

:house_with_garden: housekeeping

@DavidGOrtega [suggested on slack](https://iterativeai.slack.com/archives/C0249LW0HAQ/p1631731761006400): > Hey guys. Reviewing OpenMlOps they are exposing the models via Seldon and Ambassador API Gateway. This is similar to the QuaroML stack that I did...

deploy
plugins

Add https://bandit.readthedocs.io/en/latest/ to pre-commit

hacktoberfest
:house_with_garden: housekeeping
p3-low

I'm using a link type model that is in another GitHub repository. I refer to an existing branch, but I get the `RevisionNotFound` exception. The problem is in the GitHub...

bug
git
p2-medium

Consider the following: ```python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from mlem.api import save X, y = load_iris(return_X_y=True, as_frame=True) X_train, X_test, y_train, y_test =...

enhancement
p2-medium
data-format
❓ type: question

This is an issue to track breaking changes in next MLEM version. So far on the list: - Deployment 2.0 - ?

release
breaking-change

As reported by @alex000kim, `.mlem/env` gets ignored if you use the [standard Python gitignore file](https://github.com/github/gitignore/blob/main/Python.gitignore). To avoid this, we need to rename `.mlem/env` to `.mlem/environment` or something else.

.mlem/
git

Examples from the docs: `$ mlem pack rf pip -c target=build/ -c package_name=example_mlem_get_started` `$ mlem apply-remote http test_x.csv -c host="0.0.0.0" -c port=8080 --json` `$ mlem create env heroku staging -c...

ux
cli

(This is a follow up of a call with @aguschin) In general, ML prediction consists of: 1. Features engineering 1. Features preprocessing 2. Model fitting Once these steps are completed,...

use-case

Sometimes python package name is not the same as it's module name (eg `sklearn` vs `scikit-learn`). For that we have `MODULE_PACKAGE_MAPPING` in `mlem.core.requirements` that you can update from code. But...

bug
serialization