MLEM now offers deployment to Kubernetes and Sagemaker with a single command.
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, 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: 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 Docker Hub, 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 a model within a CI/CD pipeline. For this, MLEM includes a number of ready-to-use integrations with popular deployment platforms.
To trigger the publishing or deploying of a new version, you usually create a 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:
$ mlem declare builder pip build-to-pip \ --package_name=mypackagename \ --target=package 💾 Saving builder to build-to-pip.mlem
And then use that declaration in the CI job (e.g. with GitHub Actions):
# .github/workflows/publish.yml name: publish-my-model on: push: tags jobs: run: runs-on: [ubuntu-latest] steps: - uses: actions/[email protected] - uses: actions/setup-[email protected] - name: build run: | pip3 install -r requirements.txt mlem build --load build-to-pip.mlem --model my-model - name: publish run: | sh upload_to_pypi.sh package
Learn more about building (packaging) ML models here.
The deployment scenario is similar. First you need to create environment and deployment declarations, and commit them to Git:
$ mlem declare deployment heroku myservice \ --app_name=mlem-deployed-in-ci \ --model=my-model \ --env=staging 💾 Saving deployment to myservice.mlem
Then create and commit the CI pipeline (e.g. with GH Actions):
# .github/workflows/publish.yml name: publish-my-model on: push: tags jobs: run: runs-on: [ubuntu-latest] steps: - uses: actions/[email protected] - uses: actions/setup-[email protected] - name: pack run: | pip3 install -r requirements.txt mlem deployment run --load myservice.mlem --model my-model
Learn more about deploying ML models here.