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content/docs/start/data-pipelines.md

@@ -292,18 +292,17 @@ prepare:
 DVC pipelines (`dvc.yaml` file, `dvc run`, and `dvc repro` commands) solve a few
 important problems:
 
-- _Automation_ - run a sequence of steps in a "smart" way that makes iterating on
-  your project faster. DVC caches "runs" and results in stages and automatically
-  determines which parts of a project need to be run to avoid unnecessary
-  re-runs.
+- _Automation_ - run a sequence of steps in a "smart" way that makes iterating
+  on your project faster. DVC caches "runs" and results in stages and
+  automatically determines which parts of a project need to be run to avoid
+  unnecessary re-runs.
 - _Reproducibility_ - `dvc.yaml` and `dvc.lock` files describe what data to use
-  and which commands will generate the pipeline results (such as an ML
-  model). Storing these
-  files in Git makes it easy to version and share.
+  and which commands will generate the pipeline results (such as an ML model).
+  Storing these files in Git makes it easy to version and share.
 - _Continuous Delivery and Continuous Integration (CI/CD) for ML_ - reproducible
-  ML pipelines allow CI/CD systems to retrain models on fresh
-  datasets with identical training, save the results, and even produce reports
-  about the whole process. See [CML.dev](https://cml.dev/) for some examples.
+  ML pipelines allow CI/CD systems to retrain models on fresh datasets with
+  identical training, save the results, and even produce reports about the whole
+  process. See [CML.dev](https://cml.dev/) for some examples.
 
 ## Visualize