ai_lambda_model_inference
Differences
This shows you the differences between two versions of the page.
| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| ai_lambda_model_inference [2025/05/28 00:18] – [Architecture Overview] eagleeyenebula | ai_lambda_model_inference [2025/05/28 00:22] (current) – [AI Lambda Model Inference] eagleeyenebula | ||
|---|---|---|---|
| Line 1: | Line 1: | ||
| ====== AI Lambda Model Inference ====== | ====== AI Lambda Model Inference ====== | ||
| - | * **[[https:// | + | **[[https:// |
| The **Lambda Model Inference** module leverages AWS Lambda functions to enable serverless execution of machine learning model inference. This integration utilizes AWS services like S3 for model storage and Kinesis for real-time data streams, ensuring a scalable and cost-effective architecture for deploying AI models in production. | The **Lambda Model Inference** module leverages AWS Lambda functions to enable serverless execution of machine learning model inference. This integration utilizes AWS services like S3 for model storage and Kinesis for real-time data streams, ensuring a scalable and cost-effective architecture for deploying AI models in production. | ||
| Line 66: | Line 66: | ||
| Below is the implementation of the **Lambda handler**, which ties together model retrieval from S3 and performing predictions. | Below is the implementation of the **Lambda handler**, which ties together model retrieval from S3 and performing predictions. | ||
| - | ```python | + | < |
| + | python | ||
| import boto3 | import boto3 | ||
| import json | import json | ||
| Line 94: | Line 95: | ||
| ' | ' | ||
| } | } | ||
| - | ``` | + | </ |
| - | + | ||
| - | ### Key Points: | + | |
| - | - **Input Event**: Captures the bucket name, model key, and input data for inference. | + | |
| - | - **Model Retrieval**: | + | |
| - | - **Inference**: | + | |
| - | + | ||
| - | --- | + | |
| + | **Key Points:** | ||
| + | * **Input Event**: Captures the bucket name, model key, and input data for inference. | ||
| + | * **Model Retrieval**: | ||
| + | * **Inference**: | ||
| ===== Advanced Usage Examples ===== | ===== Advanced Usage Examples ===== | ||
| Below are examples and extended implementations to adapt the Lambda model inference system for real-world deployment and other advanced workflows. | Below are examples and extended implementations to adapt the Lambda model inference system for real-world deployment and other advanced workflows. | ||
| - | |||
| - | --- | ||
| - | |||
| ==== Example 1: Deploying a Lambda Function ==== | ==== Example 1: Deploying a Lambda Function ==== | ||
| Line 251: | Line 246: | ||
| ===== Best Practices ===== | ===== Best Practices ===== | ||
| - | 1. **Secure Your S3 Buckets**: | + | **Secure Your S3 Buckets**: |
| - | Use bucket policies or encryption to secure your model storage. | + | * Use bucket policies or encryption to secure your model storage. |
| - | + | ||
| - | 2. **Monitor Lambda Execution**: | + | |
| - | Use AWS CloudWatch for monitoring execution times, errors, and logs to troubleshoot issues quickly. | + | |
| - | + | ||
| - | 3. **Leverage IAM Roles**: | + | |
| - | | + | |
| - | 4. **Optimize Model Size**: | + | **Monitor Lambda Execution**: |
| - | Ensure that the serialized model size allows | + | * Use AWS CloudWatch |
| - | 5. **Enable Autoscaling for Kinesis**: | + | **Leverage IAM Roles**: |
| - | Use Kinesis' | + | * Attach least-privilege IAM roles to Lambda functions for secure access |
| - | --- | + | **Optimize Model Size**: |
| + | * Ensure that the serialized model size allows for quick downloads during inference. | ||
| + | **Enable Autoscaling for Kinesis**: | ||
| + | * Use Kinesis' | ||
| ===== Conclusion ===== | ===== Conclusion ===== | ||
ai_lambda_model_inference.1748391517.txt.gz · Last modified: 2025/05/28 00:18 by eagleeyenebula
