Create Bitbucket getting started guide (#257)

* Create Bitbucket getting started guide

* Apply suggestions from code review

Co-authored-by: Daniel Barnes <[email protected]>

* Rename confusion_matrix.png to plot.png

* Apply suggestions from code review

Co-authored-by: Daniel Barnes <[email protected]>

* Add token instructions

* Add more workarounds to the instructions

Co-authored-by: Daniel Barnes <[email protected]>

content/docs/sidebar.json

@@ -17,6 +17,10 @@
       {
         "label": "GitLab",
         "slug": "gitlab"
+      },
+      {
+        "label": "Bitbucket",
+        "slug": "bitbucket"
       }
     ]
   },

content/docs/start/bitbucket.md

@@ -0,0 +1,78 @@
+# Get Started with CML on Bitbucket
+
+Here, we'll walk through a tutorial to start using CML. For simplicity, we'll
+show the demo in Bitbucket Pipelines, but instructions are pretty similar for
+all the supported CI systems.
+
+1. Fork our
+   [example project repository](https://bitbucket.org/iterative-ai/example-cml).
+
+   ![](/img/bitbucket_fork_cml_project.png)
+
+   The following steps can all be done in the Bitbucket browser interface.
+   However, to follow along the commands, we recommend cloning your fork to your
+   local workstation:
+
+   ```bash
+   git clone https://bitbucket.org/<your-username>/example-cml
+   ```
+
+2. ⚠️ Follow
+   [these instructions](https://cml.dev/doc/self-hosted-runners?tab=Bitbucket#personal-access-token)
+   to configure a Bitbucket token for CML.
+3. ⚠️ Follow
+   [these instructions](https://cml.dev/doc/ref/send-comment#bitbucket) to
+   enable the Pull Request Commit Links application.
+
+4. To create a CML workflow, copy the following into a new file on your main
+   branch, `bitbucket-pipelines.yml`:
+
+   ```yaml
+   image: iterativeai/cml:0-dvc2-base1
+   pipelines:
+     default:
+       - step:
+           name: Train model
+           script:
+             - pip install -r requirements.txt
+             - python train.py
+
+             - cat metrics.txt > report.md
+             - cml-publish plot.png --md >> report.md
+             - cml-send-comment report.md
+   ```
+
+5. In your text editor of choice, edit line 15 of `train.py` to `depth = 12`.
+
+6. Commit and push the changes:
+
+   ```bash
+   git checkout -b experiment
+   git add . && git commit -m "modify forest depth"
+   git push origin experiment
+   ```
+
+7. In Bitbucket, create a Pull Request to compare the `experiment` branch to
+   `master`.
+
+   ![](/img/bitbucket_make_pr.png)
+
+   Shortly, you should see a comment from your user appear in the Pull Request
+   with your CML report. This is a result of the `cml send-comment` command in
+   your workflow.
+
+   ![](/img/bitbucket_cml_first_report.png)
+
+This is the gist of the CML workflow: when you push changes to your Bitbucket
+repository, the workflow in your `bitbucket-pipelines.yml` file gets run and a
+report generated.
+
+CML commands let you display relevant results from the workflow, like model
+performance metrics and vizualizations, in Bitbucket checks and comments. What
+kind of workflow you want to run, and want to put in your CML report, is up to
+you.
+
+## Final Solution
+
+An example of what your repository should look like now can be found at
+[`iterative-ai/cml-base-case`](https://bitbucket.org/iterative-ai/cml-base-case).