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Put up a deprecation warning.\r\n- Fix `spark.log_model` bug w/r.t. `jars` arg\r\n- Fix datetime arg to `projects.run`\r\n- Remove & rename some `projects.run` parameters\r\n- Mark `azureml.build_image` as experimental\r\n- Some small docs changes\r\n \r\n## How is this patch tested?\r\n \r\n- Existing unit tests\r\n- Built and checked docs to see that the experimental note and the deprecation warning show up. \r\n \r\n## Release Notes\r\n \r\n### Is this a user-facing change? \r\n\r\n- [ ] No. You can skip the rest of this section.\r\n- [X] Yes. Give a description of this change to be included in the release notes for MLflow users.\r\n \r\nCleaned up some python and R API to make them stable for 1.0:\r\n- Introduced `pyfunc.load_model` to be consistent with other Model modules. `pyfunc.load_pyfunc` will be deprecated in the near future.\r\n- Fixed `spark.log_model` bug w/r.t. `jars` arg\r\n- Removed & renamed some `projects.run` parameters for generality and consistency\r\n- Marked `azureml.build_image` as experimental\r\n \r\n### What component(s) does this PR affect?\r\n \r\n- [ ] UI\r\n- [X] CLI \r\n- [X] API \r\n- [ ] REST-API \r\n- [ ] Examples \r\n- [ ] Docs\r\n- [ ] Tracking\r\n- [X] Projects \r\n- [ ] Artifacts \r\n- [X] Models \r\n- [ ] Scoring \r\n- [X] Serving\r\n- [X] R\r\n- [ ] Java\r\n- [X] Python\r\n\r\n### How should the PR be classified in the release notes? Choose one:\r\n \r\n- [X] `rn/breaking-change` - The PR will be mentioned in the \"Breaking Changes\" 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