Generalized Omni-dimensional Development

User Tools

Site Tools

Trace:
ai_inference_service

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision		Previous revision
ai_inference_service [2025/05/27 16:15] – [Key Features] eagleeyenebulaai_inference_service [2025/06/23 18:49] (current) – [AI Inference Service] eagleeyenebula
Line 1: Line 1:
 ====== AI Inference Service ====== ====== AI Inference Service ======
-* **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:+[[https://autobotsolutions.com/aurora/wiki/doku.php?id=ai_inference_service|Wiki]]: [[https://autobotsolutions.com/god/templates/ai_inference_service.html|Framework]]: [[https://github.com/AutoBotSolutions/Aurora/blob/Aurora/ai_inference_service.py|GitHub]]: [[https://autobotsolutions.com/artificial-intelligence/ai-inference-service-scalable-and-configurable-inference-for-ai-ml-models/|Article]]: 
 + 
 The **AI Inference Service** provides a streamlined, configurable interface for leveraging trained AI models to make predictions on new inputs. With support for pre-processing, post-processing, and error handling, this class is designed for efficient deployment in a variety of AI and machine learning use cases. The **AI Inference Service** provides a streamlined, configurable interface for leveraging trained AI models to make predictions on new inputs. With support for pre-processing, post-processing, and error handling, this class is designed for efficient deployment in a variety of AI and machine learning use cases.
  
Line 8: Line 10:
 ------------------------------------------------------------- -------------------------------------------------------------
  
 +Its modular architecture allows developers to plug in different models and workflows without rewriting core logic, making it ideal for rapid prototyping and scalable production environments. Whether integrating into a real-time API or powering batch inference pipelines, the service ensures consistency and reliability across diverse data contexts.
 +
 +Moreover, by encapsulating complex inference workflows into a clean, reusable abstraction, the AI Inference Service promotes best practices in maintainable AI system design. It not only enhances model interoperability and deployment agility but also helps teams manage evolving requirements with minimal overhead accelerating the path from experimentation to value delivery.
 ===== Purpose ===== ===== Purpose =====
  
Line 44: Line 49:
 ===== Initialization ===== ===== Initialization =====
  
-The `InferenceService` class is initialized with a trained model and an optional configuration dictionary.+The **InferenceService** class is initialized with a trained model and an optional configuration dictionary.
  
-```python+<code> 
 +python
 from my_inference_service import InferenceService from my_inference_service import InferenceService
- +</code> 
-# Example: Initialize with a trained model and configuration+**Example: Initialize with a trained model and configuration** 
 +<code>
 trained_model = load_trained_model()  # Assume you have a trained model loading function trained_model = load_trained_model()  # Assume you have a trained model loading function
 config = {"threshold": 0.5}  # Example configuration with a prediction threshold config = {"threshold": 0.5}  # Example configuration with a prediction threshold
 service = InferenceService(trained_model, config) service = InferenceService(trained_model, config)
-``` +</code>
- +
-- **trained_model**: The AI model (e.g., Scikit-learn, TensorFlow, PyTorch) that has been trained and is ready for inference. +
-- **config**: (Optional) A dictionary of additional settings—such as thresholds or pre/post-processing flags.+
  
----+**trained_model**:  
 +The AI model (e.g., Scikit-learn, TensorFlow, PyTorch) that has been trained and is ready for inference.
  
 +**config**: 
 +(Optional) A dictionary of additional settings such as thresholds or pre/post-processing flags.
 ===== Core Methods ===== ===== Core Methods =====
  
-### `predict(input_data)`+**predict(input_data)**
  
-The `predict` method takes raw input data, uses the trained model for inference, and applies optional post-processing based on the configuration.+The **predict** method takes raw input data, uses the trained model for inference, and applies optional post-processing based on the configuration.
  
 **Parameters**: **Parameters**:
-  - `input_data`: Input data for prediction, typically in **Pandas DataFrame** or **NumPy array** formats.+  * **input_data**: Input data for prediction, typically in **Pandas DataFrame** or **NumPy array** formats.
  
 **Returns**: **Returns**:
-  - Predictions: Either raw model outputs or processed predictions (e.g., binary classification results).+  * Predictions: Either raw model outputs or processed predictions (e.g., binary classification results).
  
 **Post-Processing**: **Post-Processing**:
-  An optional post-processing step applies thresholds, if specified in the configuration. +  * An optional post-processing step applies thresholds, if specified in the configuration.
- +
---- +
 ===== Usage Examples ===== ===== Usage Examples =====
  
 Below are examples showcasing a variety of use cases for the **InferenceService**: Below are examples showcasing a variety of use cases for the **InferenceService**:
- 
---- 
- 
 ==== Example 1: Single Input Prediction with Threshold ==== ==== Example 1: Single Input Prediction with Threshold ====
  
 This example demonstrates how to make predictions on a single batch of input data with threshold-based processing. This example demonstrates how to make predictions on a single batch of input data with threshold-based processing.
  
-```python+<code> 
 +python
 import numpy as np import numpy as np
 from my_inference_service import InferenceService from my_inference_service import InferenceService
- +</code> 
-# Initialize with a mock trained model and threshold configuration+**Initialize with a mock trained model and threshold configuration** 
 +<code>
 class MockModel: class MockModel:
     def predict(self, input_data):     def predict(self, input_data):
Line 99: Line 102:
 config = {"threshold": 0.5}  # Use a threshold of 0.5 for binary classification config = {"threshold": 0.5}  # Use a threshold of 0.5 for binary classification
 service = InferenceService(trained_model, config) service = InferenceService(trained_model, config)
- +</code> 
-# Input data (NumPy array)+**Input data (NumPy array)** 
 +<code>
 input_data = np.array([[1, 2], [2, 3], [3, 4], [4, 5]]) input_data = np.array([[1, 2], [2, 3], [3, 4], [4, 5]])
- +</code> 
-# Make predictions+**Make predictions** 
 +<code>
 predictions = service.predict(input_data) predictions = service.predict(input_data)
 print(predictions)  # Output: [1, 0, 1, 0] (after applying threshold) print(predictions)  # Output: [1, 0, 1, 0] (after applying threshold)
-```+</code>
  
 **Explanation**: **Explanation**:
-- **MockModel** simulates a trained model for demonstration purposes. +  * **MockModel** simulates a trained model for demonstration purposes. 
-- The **threshold** is applied to convert raw numerical predictions into binary classification results. +  * The **threshold** is applied to convert raw numerical predictions into binary classification results.
- +
---- +
 ==== Example 2: Batch Predictions in Production ==== ==== Example 2: Batch Predictions in Production ====
  
 Demonstrates how to use the **InferenceService** to handle batch processing during production. Demonstrates how to use the **InferenceService** to handle batch processing during production.
  
-```python+<code> 
 +python
 import pandas as pd import pandas as pd
 from my_inference_service import InferenceService from my_inference_service import InferenceService
- +</code> 
-# Initialize with a trained model+**Initialize with a trained model** 
 +<code>
 trained_model = load_trained_model() trained_model = load_trained_model()
 service = InferenceService(trained_model) service = InferenceService(trained_model)
- +</code> 
-# Batch input data (Pandas DataFrame)+**Batch input data (Pandas DataFrame)** 
 +<code>
 input_data = pd.DataFrame({ input_data = pd.DataFrame({
     "feature_1": [1.5, 2.5, 3.0],     "feature_1": [1.5, 2.5, 3.0],
     "feature_2": [3.5, 4.1, 1.2]     "feature_2": [3.5, 4.1, 1.2]
 }) })
- +</code> 
-# Perform batch inference+**Perform batch inference** 
 +<code>
 predictions = service.predict(input_data) predictions = service.predict(input_data)
 print(predictions)  # Output: [Raw predictions from the model] print(predictions)  # Output: [Raw predictions from the model]
-```+</code>
  
 **Explanation**: **Explanation**:
-- Input data is provided as a **Pandas DataFrame**, which is a common format for tabular data. +  * Input data is provided as a **Pandas DataFrame**, which is a common format for tabular data. 
-- The model processes the batch data and returns raw predictions. +  * The model processes the batch data and returns raw predictions.
- +
---- +
 ==== Example 3: Extending with Advanced Post-Processing ==== ==== Example 3: Extending with Advanced Post-Processing ====
  
-This example shows how to extend `InferenceService` for additional post-processing logic, such as multi-class classification.+This example shows how to extend **InferenceService** for additional post-processing logic, such as multi-class classification.
  
-```python+<code> 
 +python
 class AdvancedInferenceService(InferenceService): class AdvancedInferenceService(InferenceService):
     """     """
Line 163: Line 167:
         predicted_classes = [class_labels[p] for p in predictions]         predicted_classes = [class_labels[p] for p in predictions]
         return predicted_classes         return predicted_classes
 +</code>
  
- +**Example usage** 
-# Example usage+<code>
 trained_model = load_trained_classification_model() trained_model = load_trained_classification_model()
 service = AdvancedInferenceService(trained_model) service = AdvancedInferenceService(trained_model)
Line 174: Line 179:
 predicted_classes = service.predict_with_classes(input_data, class_labels) predicted_classes = service.predict_with_classes(input_data, class_labels)
 print(predicted_classes)  # Output: ['Class B', 'Class A', 'Class C'] print(predicted_classes)  # Output: ['Class B', 'Class A', 'Class C']
-```+</code>
  
 **Explanation**: **Explanation**:
-- Extends the `InferenceService` to match model predictions with their corresponding class labels. +   * Extends the **InferenceService** to match model predictions with their corresponding class labels. 
-- Demonstrates the modularity and extensibility of the system. +   * Demonstrates the modularity and extensibility of the system.
- +
---- +
 ==== Example 4: Logging for Debugging and Metrics ==== ==== Example 4: Logging for Debugging and Metrics ====
  
-Shows how the logging functionality in `InferenceService` helps track inputs, outputs, and errors during inference.+Shows how the logging functionality in **InferenceService** helps track inputs, outputs, and errors during inference.
  
-```python+<code> 
 +python
 try: try:
     predictions = service.predict(input_data)     predictions = service.predict(input_data)
 except Exception as e: except Exception as e:
     logging.error(f"Inference failed: {e}")     logging.error(f"Inference failed: {e}")
-```+</code>
  
 **Features**: **Features**:
-- Logs input data, configuration settings, prediction outputs, and errors for comprehensive debugging. +  * Logs input data, configuration settings, prediction outputs, and errors for comprehensive debugging. 
-- Ensures production-grade reliability by tracking system behavior. +  * Ensures production-grade reliability by tracking system behavior.
- +
---- +
 ===== Use Cases ===== ===== Use Cases =====
  
 1. **Generic Model Serving**:   1. **Generic Model Serving**:  
-   Use the service as a centralized interface for AI model inference across various input types and configurations.+   * Use the service as a centralized interface for AI model inference across various input types and configurations.
  
 2. **Batch Processing**:   2. **Batch Processing**:  
-   Handle batch inference workloads for applications like image processing, natural language processing, and analytics.+   * Handle batch inference workloads for applications like image processing, natural language processing, and analytics.
  
 3. **Binary Classification**:   3. **Binary Classification**:  
-   Easily configure thresholds for binary classification tasks to refine raw model predictions.+   * Easily configure thresholds for binary classification tasks to refine raw model predictions.
  
 4. **Multi/Custom Classifications**:   4. **Multi/Custom Classifications**:  
-   Extend functionality for categorizing predictions into defined class labels.+   * Extend functionality for categorizing predictions into defined class labels.
  
 5. **Production-Ready Systems**:   5. **Production-Ready Systems**:  
-   Leverage logging and error handling for real-time diagnostics and production monitoring. +   * Leverage logging and error handling for real-time diagnostics and production monitoring.
- +
---- +
 ===== Best Practices ===== ===== Best Practices =====
  
 1. **Error Logging**:   1. **Error Logging**:  
-   Capture and log all exceptions during inference for debugging and resolution.+   * Capture and log all exceptions during inference for debugging and resolution.
  
 2. **Threshold Experimentation**:   2. **Threshold Experimentation**:  
-   Experiment with various threshold values to optimize classification performance.+   * Experiment with various threshold values to optimize classification performance.
  
 3. **Data Validation**:   3. **Data Validation**:  
-   Verify and sanitize input data to ensure compatibility with the trained model.+   * Verify and sanitize input data to ensure compatibility with the trained model.
  
 4. **Extensibility**:   4. **Extensibility**:  
-   Customize the service to include domain-specific features (e.g., multi-class classification, real-time alerts).+   * Customize the service to include domain-specific features (e.g., multi-class classification, real-time alerts).
  
 5. **Efficient Batching**:   5. **Efficient Batching**:  
-   Optimize input data batching for better throughput in high-volume deployments. +   * Optimize input data batching for better throughput in high-volume deployments.
- +
---- +
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Inference Service** provides robust, configurable, and extensible infrastructure for AI model inference. By simplifying and centralizing the inference process, it accelerates production deployments while offering flexibility for domain-specific extensions. With built-in logging, error handling, and an extensible design, this service is an invaluable tool for AI researchers, developers, and production engineers. The **AI Inference Service** provides robust, configurable, and extensible infrastructure for AI model inference. By simplifying and centralizing the inference process, it accelerates production deployments while offering flexibility for domain-specific extensions. With built-in logging, error handling, and an extensible design, this service is an invaluable tool for AI researchers, developers, and production engineers.
  
ai_inference_service.1748362514.txt.gz · Last modified: 2025/05/27 16:15 by eagleeyenebula