MLOps V1

What should you do?

•  Import the model into Vertex AI Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.

• Save the model files in a Cloud Storage bucket. Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.

• Save the model files in a VM. Load the model files each time there is a prediction request, and run an inference job on the VM.

• Import the model into Vertex AI Model Registry. Create a Vertex AI endpoint that hosts the model, and make online inference requests.

What should you do?

• Implement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.

• Create a Vertex AI experiment. Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex AI SDK.

• Implement all models in Vertex AI Pipelines Create a Vertex AI experiment, and associate all pipeline runs with that experiment.

• Store all model parameters and metrics as model metadata by using the Vertex AI Metadata API.

What should you do?

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What should you do?

• Submit a request to raise your project quota to ensure that multiple prediction services can run concurrently.

• Turn off auto-scaling for the online prediction service of your new model. Use manual scaling with one node always available.

• Remove your new model from the production environment. Compare the new model and existing model codes to identify the cause of the performance bottleneck.

• Remove your new model from the production environment. For a short trial period, send all incoming prediction requests to BigQuery. Request batch predictions from your new model, and then use the Data Labeling Service to validate your model’s performance before promoting it to production.

What should you do?

• Import the model into Vertex AI and deploy it to a Vertex AI endpoint. On Vertex AI Pipelines, create a pipeline that uses the DataflowPythonJobOp and the ModelBacthPredictOp components.

• Import the model into Vertex AI and deploy it to a Vertex AI endpoint. Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint, and then uploads predictions to a BigQuery table.

• Import the model into Vertex AI. On Vertex AI Pipelines, create a pipeline that uses the DataflowPythonJobOp and the ModelBatchPredictOp components.

• Import the model into BigQuery. Implement the data processing logic in a SQL query. On Vertex AI Pipelines create a pipeline that uses the BigquervQueryJobOp and the BigqueryPredictModelJobOp components.

What should you do?

• Use the features for monitoring. Set a monitoring-frequency value that is higher than the default.

• Use the features for monitoring. Set a prediction-sampling-rate value that is closer to 1 than 0.

• Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.

• Use the features and the feature attributions for monitoring. Set a prediction-sampling-rate value that is closer to 0 than 1.

What should you do?

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What should you do?

•  Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.

• Enable autoscaling of the online serving nodes in your featurestore.

• Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex AI endpoint.

• Increase the worker_count in the ImportFeatureValues request of your batch ingestion job.

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What should you do?

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