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Model registry blog post #159
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strickvl
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May 26, 2023
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| title: "Productionalizing LangChain and LlamaIndex with a ZenML MLOps Pipeline to Help Community Slack Support" | ||
| description: "We decided to explore how the emerging technologies around Large Language Models (LLMs) could seamlessly fit into ZenML's MLOps workflows and standards. We created and deployed a Slack bot to provide community support." |
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These both need changing..
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| publish_date: April 10, 2023 | ||
| date: 2023-03-31T00:02:00Z |
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These need updating, and also the file name
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| thumbnail: /assets/posts/slackbot/slackbot-small.png | ||
| image: | ||
| path: /assets/posts/slackbot/slackbot.png |
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| **Last updated:** March 31, 2023 | ||
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| *](/assets/posts/slackbot/slackbot-small.png) |
| path: /assets/posts/slackbot/slackbot.png | ||
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| **Last updated:** March 31, 2023 |
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| ## Benefits of Model Registries in Your Workflow | ||
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| ## Benefits of Model Registries in Your Workflow | |
| ## Benefits of Model Registries in Your Workflow | |
| Some good reasons why you might want to work with a model registry as part of your stack include: | |
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| **Simplified Model Lifecycle Management:** Model registries enable you to register, track, and version your models in a central repository, store metadata and runtime dependencies, and build automated pipelines for continuous integration, delivery, and training. | ||
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| **Improved Collaboration:** Model registries provide a central UI for teams to collaborate on models, bridging the gap between machine learning and operations. This unifying component reduces friction in the hand-off of production-ready models from experimentation to production environments. |
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Btw, side comment, but how is the model registry represented in the dashboard?
| zenml model-registry register mlflow_model_registry --flavor=mlflow | ||
| ``` | ||
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| Utilizing MLflow Model Registry with ZenML |
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| Utilizing MLflow Model Registry with ZenML | |
| ### Using the MLflow Model Registry with ZenML |
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| ### Using the Built-in Step | ||
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| The MLflow Model Registry is a built-in step in ZenML pipelines. To use it, simply add the `model_register` step to your pipeline: |
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| The MLflow Model Registry is a built-in step in ZenML pipelines. To use it, simply add the `model_register` step to your pipeline: | |
| The MLflow Model Registry is a built-in step in ZenML pipelines. To use it, simply add the `mlflow_model_register_step` step to your pipeline: |
Shouldn't it be this?
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| ## Conclusion | ||
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| Incorporating a model registry into your ZenML MLOps stack offers numerous benefits. It can speed up deployment, simplify model lifecycle management, enhance collaboration, and improve model governance. By integrating the MLflow Model Registry with ZenML, you can further streamline the management and deployment of your machine learning models. Embrace the power of model registries in your machine learning workflows and take advantage of their comprehensive capabilities. |
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You should include a link to the docs for model registry, also point people to the example where they can get going with it, and then also include a link to slack for if people have questions.
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