Merge pull request #137 from iterative/cases/cicd-mlem

cases: copy edit CI/CD for ML

content/docs/use-cases/cicd.md

@@ -3,35 +3,32 @@
 Applying DevOps methodologies to machine learning (MLOps) and data management
 (DataOps) is increasingly common. This means resource orchestration
 (provisioning servers for model training), model testing (validating model
-inference), and model deployment to production, as well as monitoring &
-feedback. MLEM provides you a simple way to publish or deploy your machine
-learning models in CI/CD pipelines.
+inference), and model deployment, as well as monitoring and feedback. MLEM
+provides a simple way to publish or deploy your machine learning models with
+CI/CD pipelines.
 
-- **Packaging and publishing models**: It is a common case when you need to wrap
-  your machine learning model into some specific format and publish it in some
-  registry. Examples include turning your ML model into a Python package and
-  publishing it on PyPi, or building a docker image and pushing it to DockerHub,
-  or just exporting your model to ONNX and publishing it as an artifact to
-  Artifactory.
+- **Packaging and publishing models**: A common need is when you need to wrap
+  your ML model in a specific format and publish it in some registry. Examples
+  include turning your ML model into a Python package and publishing it on PyPi,
+  building a docker image and pushing it to DockerHub, or just exporting your
+  model to ONNX and publishing it as an artifact to Artifactory.
 
-- **Deploying models**: Another common scenario is when you want to deploy your
-  model in your CI/CD pipeline. MLEM can help you with that by providing a
-  number of ready-to-use integrations with popular deployment platforms.
+- **Deploying models**: Another common scenario is when you want to deploy a
+  model within a CI/CD pipeline. For this, MLEM includes a number of
+  ready-to-use integrations with popular deployment platforms.
 
-## Examples
-
-### Build and publish
+## Package and publish an ML model
 
 To trigger the publishing or deploying of a new version, you usually create a
-Git tag that kicks off CI process. To make building and deployment process
-consistent you can create and commit MLEM declaration:
+Git tag that kicks off the CI process. To make this build process consistent
+with future deployment, you can create and commit an MLEM declaration:
 
 ```cli
 $ mlem declare builder pip -c package_name=mypackagename -c target=package build-to-pip
 💾 Saving builder to build-to-pip.mlem
 ```
 
-And then use that declaration in CI:
+And then use that declaration in the CI job (e.g. with GitHub Actions):
 
 ```yaml
 # .github/workflows/publish.yml
@@ -59,12 +56,13 @@ jobs:
           sh upload_to_pypi.sh package
 ```
 
-Learn more about building in [Get Started](/doc/get-started/building).
+Learn more about building (packaging) ML models
+[here](/doc/get-started/building).
 
-### Deploy
+## Deploy an ML model
 
-Example with deployment is quite similar. First you need to create environment
-and deployment declaration and commit them to Git:
+The deployment scenario is similar. First you need to create environment and
+deployment declarations, and commit them to Git:
 
 ```cli
 $ mlem declare env heroku staging
@@ -74,7 +72,7 @@ $ mlem declare deployment heroku myservice -c app_name=mlem-deployed-in-ci -c mo
 💾 Saving deployment to myservice.mlem
 ```
 
-Then create and commit CI pipeline, e.g. in GH Actions:
+Then create and commit the CI pipeline (e.g. with GH Actions):
 
 ```yaml
 # .github/workflows/publish.yml
@@ -98,5 +96,4 @@ jobs:
           mlem deployment my-model --load myservice.mlem
 ```
 
-Learn more about deploying in Get Started
-[Get Started](/doc/get-started/deploying).
+Learn more about deploying ML models [here](/doc/get-started/deploying).