Create Bitbucket getting started guide

content/docs/start/bitbucket.md

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+# 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. To create a CML workflow, copy the following into a new file,
+   `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 confusion_matrix.png --md >> report.md
+              - cml-send-comment report.md
+   ```
+
+3. In your text editor of choice, edit line 16 of `train.py` to `depth = 12`.
+
+4. Commit and push the changes:
+
+   ```bash
+   git checkout -b experiment
+   git add . && git commit -m "modify forest depth"
+   git push origin experiment
+   ```
+
+5. 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).