How to create a version of the model?

/ / Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Serverless predictions at scale

Creating a version of a machine learning model in Google Cloud Platform (GCP) is a critical step in deploying models for serverless predictions at scale. A version in this context refers to a specific instance of a model that can be used for predictions. This process is integral to managing and maintaining different iterations of a machine learning model, allowing developers to improve and update models while ensuring stability and consistency in production environments.

Understanding Google Cloud AI Platform

Google Cloud AI Platform provides a comprehensive suite of tools for developing, training, and deploying machine learning models. It supports a variety of frameworks such as TensorFlow, scikit-learn, and XGBoost, and facilitates the deployment of models in a scalable, serverless manner. The AI Platform is designed to streamline the process of moving models from development to production, providing capabilities like versioning, monitoring, and resource management.

Steps to Create a Version of a Model

Creating a version of a model involves several steps, which can be broadly categorized into preparing the model, deploying it to AI Platform, and managing versions. Below is a detailed walkthrough of these steps:

Step 1: Prepare the Model

Before deploying a model, it must be trained and exported in a format compatible with Google Cloud AI Platform. For instance, if using TensorFlow, the model should be saved as a SavedModel directory. This directory contains the model architecture, weights, and other necessary metadata.

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Step 2: Upload the Model to Google Cloud Storage (GCS)


Once the model is prepared, it must be uploaded to a Google Cloud Storage bucket. AI Platform requires models to be stored in GCS for deployment.


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Step 3: Deploy the Model to AI Platform


With the model stored in GCS, the next step is to create a model resource in AI Platform. This is done via the Google Cloud Console, the `gcloud` command-line tool, or the AI Platform client libraries.


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Step 4: Create a Model Version


Creating a version involves specifying the location of the model in GCS and configuring various parameters such as the machine type, framework, and runtime version. This step is important as it allows the deployment of different versions of the same model, facilitating A/B testing, rollback, and gradual rollout.


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Step 5: Manage Model Versions


Once a version is created, it can be managed through the AI Platform. This includes setting a default version, deleting old versions, and monitoring the performance of each version.

- Setting a Default Version: The default version is used for prediction requests if no version is specified. This can be set using the `gcloud` command:

  
bash
  gcloud ai-platform versions set-default v1 --model=my_model
  
- Monitoring and Logging: AI Platform provides logging and monitoring tools to track the performance and usage of each model version. This is essential for understanding how the model behaves in production and identifying any issues that may arise.
- Deleting a Version: Old or unused versions can be deleted to reduce costs and manage resources efficiently:
 
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Best Practices for Model Versioning
- Use Semantic Versioning: Adopt a versioning scheme that reflects the changes made to the model, such as semantic versioning (e.g., v1.0.0, v1.1.0). This helps in understanding the nature of changes between versions.
- Automate Deployment: Incorporate model versioning into a CI/CD pipeline to automate the deployment and testing of new versions. This reduces manual errors and speeds up the release process.
- Test Before Deployment: Thoroughly test new versions in a staging environment before deploying to production. This ensures that the model performs as expected under different scenarios.
- Monitor and Iterate: Continuously monitor the performance of each model version and iterate based on feedback and performance metrics. This helps in maintaining the accuracy and relevance of the model over time.
Example Use Case
Consider a retail company using a machine learning model to predict product demand. The company regularly updates the model to incorporate new data and improve accuracy. By using AI Platform's versioning capabilities, they can deploy new versions of the model without disrupting the existing prediction service. They can also perform A/B testing by directing a portion of the traffic to the new version and comparing the results with the current version. This approach allows them to make data-driven decisions about whether to fully transition to the new model version.
Creating and managing model versions in Google Cloud AI Platform is a fundamental aspect of deploying machine learning models at scale. By following a structured approach to model versioning, organizations can ensure that their models are robust, scalable, and capable of meeting production demands. This process not only facilitates the deployment of models but also enhances the ability to iterate and improve models over time, leading to better predictive performance and business outcomes.
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