feat: Automated model approval pipeline #4528
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bigbernnn
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| def lambda_handler(event, context): | ||
| # Initialize the SageMaker client | ||
| sagemaker_client = boto3.client('sagemaker') |
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A Boto3 client is initialized from a Lambda handler. To speed up your Boto3 client initialization and minimize the operational cost of your Lambda function, we recommend that you create the client at the level of the module that contains the handler, and then reuse it between invocations.
| else: | ||
| approval_description = "Model values for the following checks does not meet threshold: " | ||
| for reason in reasons: | ||
| approval_description+= f"{reason} " |
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It appears that you are concatenating a string inside a loop. Concatenating immutable sequences will always results in a new object. This means that building up a sequence by repeated concatenation will have a quadratic runtime cost in the total sequence length. For efficiency and to get a linear runtime cost, consider appending values to a list and concatenating that list using str.join() or bytes.join() method.
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Issue #, if available:
Description of changes:
We have added an example in the sagemaker-pipelines directory under automated-model-approval. This is related to an already approved blog, which will be submitted shortly. The folder contains a pipeline being triggered by a lambda function when a new model enters the model registry. The pipeline has two steps. The first, evaluates compliance with some internal thresholds for model quality, bias and explainability metrics. The second, updates the model status and description based on these checks.
This work stems from the challenges faced a public sector customer. This is an example governance pipeline that customers will be able to expand based on their internal processes and requirements.
Testing done:
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