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Refactor Deployment page and add local / SageMaker / Azure / Databricks guides #10675
Refactor Deployment page and add local / SageMaker / Azure / Databricks guides #10675
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… deployments Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Documentation preview for 3776534 will be available here when this CircleCI job completes successfully. More info
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Signed-off-by: B-Step62 <yuki.watanabe@databricks.com> deleted: docs/source/_static/images/logos/amazon-sagemaker-logo.svg new file: docs/source/_static/images/logos/azure-ml-logo.png
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
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Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
- **Packaging Models and Code**: With MLflow, not just the model, but any supplementary code and configurations are packaged along with the deployment container. This ensures that the model can be executed seamlessly without any missing components. | ||
- **Avoid Vendor Lock-in**: MLflow provides a standard format for packaging models and unified APIs for deployment. You can easily switch between deployment targets without having to rewrite your code. |
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I'm working with a customer migrating from AWS to the Databricks stack. Would it be useful to have a demo for migrating between deployment methods?
cc @BenWilson2
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Sounds like a good follow-up! Tho I'm not very sure about the migration process as a whole. Any thoughts @BenWilson2?
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It's definitely a bit simpler :D I think for Databricks-specific instructions, we're going to have to leave those in the Databricks docs, though. We could provide links :)
Co-authored-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com> Signed-off-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Realized that AzureML and Databricks already have solid guidelines for MLflow model deployment in their documentation. Replaced the placeholder pages with direct links. |
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@B-Step62 It looks great!! I think you're doing a great work at balancing between explanation and text amount. Just left few comments :D LMK if you need another review after adding more content.
Once you have the model ready, deploying to a local server is straightforward. Use the `mlflow models serve <../cli.html#mlflow-models-serve>`_ command for a one-step deployment. | ||
This command starts a local server that listens on the specified port and serves your model. | ||
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.. tabs:: |
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Love the usage of tabs here! We could apply tabs to all similar situations for different languages or different operating systems.
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Absolutely! Only concern is if it works in the new framework... but no reason not to do that after migration:)
Co-authored-by: Serena Ruan <82044803+serena-ruan@users.noreply.github.com> Signed-off-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
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@@ -48,6 +48,19 @@ | |||
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.simple-card .header-with-image { |
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We might want to slap these logos on some existing cards too :) Let's do a follow-up to tackle that next sprint
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Sounds good, created a follow-up JIRA:)
Co-authored-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com> Signed-off-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Merging assuming all good (if there is any remaining feedbacks, can address in the follow-up PR for k8s) |
…ks guides (mlflow#10675) Signed-off-by: B-Step62 <yuki.watanabe@databricks.com> Signed-off-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com> Co-authored-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com> Co-authored-by: Serena Ruan <82044803+serena-ruan@users.noreply.github.com>
🛠 DevTools 🛠
Install mlflow from this PR
Checkout with GitHub CLI
Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
This is a first PR for deployment doc overhaul. More changes will be coming to
deployment-doc-revamp
branch.Changes in this PR
deployment/index.html
TODOs for future PRs
How is this PR tested?
Does this PR require documentation update?
Release Notes
Is this a user-facing change?
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/artifacts
: Artifact stores and artifact loggingarea/build
: Build and test infrastructure for MLflowarea/deployments
: MLflow Deployments client APIs, server, and third-party Deployments integrationsarea/docs
: MLflow documentation pagesarea/examples
: Example codearea/model-registry
: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models
: MLmodel format, model serialization/deserialization, flavorsarea/recipes
: Recipes, Recipe APIs, Recipe configs, Recipe Templatesarea/projects
: MLproject format, project running backendsarea/scoring
: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra
: MLflow Tracking server backendarea/tracking
: Tracking Service, tracking client APIs, autologgingInterface
area/uiux
: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/docker
: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy
: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows
: Windows supportLanguage
language/r
: R APIs and clientslanguage/java
: Java APIs and clientslanguage/new
: Proposals for new client languagesIntegrations
integrations/azure
: Azure and Azure ML integrationsintegrations/sagemaker
: SageMaker integrationsintegrations/databricks
: Databricks integrationsHow should the PR be classified in the release notes? Choose one:
rn/none
- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/breaking-change
- The PR will be mentioned in the "Breaking Changes" sectionrn/feature
- A new user-facing feature worth mentioning in the release notesrn/bug-fix
- A user-facing bug fix worth mentioning in the release notesrn/documentation
- A user-facing documentation change worth mentioning in the release notes