TODO: Download image from URL and run prediction Args: image_url: URL to image top_k: Number of top predicti

TODO: Implement data preprocessing and feature engineering Steps: 1. Load validated raw data 2. Handle missing values (imputation, r

TODO: Implement image preprocessing Steps: 1. Convert bytes to PIL Image 2. Resize to model input size (e.g., 224x224) 3. Normal

TODO: Preprocess image for model inference Steps to implement: 1. Convert PIL Image to numpy array 2. Normalize pixel values to [0,

TODO: Preprocess image bytes for model input Args: image_bytes: Raw image bytes (JPEG, PNG, etc.) Returns:

Generate a human-readable optimization report. Returns: Formatted report string TODO: Include: 1. Curre

Process documents through pipeline. TODO: Implement processing pipeline - Clean each document - Filter documents

Process a single document. TODO: Implement single document processing - Clean content - Check if passes filter

Profile LLM inference performance. Args: prompts: Test prompts for profiling engine: LLM engine instance

TODO: Pull the latest Production model from MLflow Model Registry Steps: 1. Connect to MLflow Model Registry 2. Get latest Productio

TODO: Pull model from MLflow Registry Steps: 1. Connect to MLflow 2. Get latest model in specified stage 3.

Query with conversation context. Args: question: Current question conversation_id: Conversation identifier

Generate answer using RAG (Retrieval-Augmented Generation). Args: request: RAG request with question api_key: API key

Kubernetes readiness probe.

Readiness check endpoint. Different from health check - indicates if the service is ready to accept traffic (model loaded, warmup comple

Record compute cost. TODO: Implement compute cost recording - Calculate cost - Create event - Update aggrega

Record and return cost. TODO: Implement cost recording - Calculate cost based on tokens - Increment cost counter

Record DAG run completion. Args: dag_id: Airflow DAG ID run_id: Airflow run ID duration_seconds:

Record DAG run start. Args: dag_id: Airflow DAG ID run_id: Airflow run ID scheduled_time: Schedu

Record data drift detection results. Args: dataset_name: Name of the dataset feature_name: Name of the featu

Record an error. TODO: Implement error recording - Increment error counter - Label with model, error type A

Record request latency. TODO: Implement latency recording - Observe duration in histogram - Label with model, endpoi

Record model evaluation metrics. Args: model_name: Name of the model version: Model version eval

Record a completed request. TODO: Implement request recording - Increment request counter - Label with model, endpoi

Record cost for a request. TODO: Implement request cost recording - Calculate cost - Create cost event - Upd

Record resource usage during pipeline execution. Args: dag_id: Airflow DAG ID task_id: Airflow task ID

Record schema validation results. Args: dataset_name: Name of the dataset expected_schema: Expected schema d

Record individual task execution. Args: dag_id: Airflow DAG ID task_id: Airflow task ID duration

Record token usage. TODO: Implement token recording - Increment input token counter - Increment output token counter

Record successful training completion. Args: model_name: Name of the trained model version: Model version

Record training failure. Args: model_name: Name of the model version: Model version error_type:

Record start of model training. Args: model_name: Name of the model being trained version: Model version

Record data validation results. Args: dataset_name: Name of the dataset validated validation_suite: Name of

Remove HTML tags from text. TODO: Implement HTML tag removal - Use regex or parser - Preserve text content - Handle nested tags

TODO: Resize image while maintaining aspect ratio Steps to implement: 1. Calculate aspect ratio 2. Resize to target size (with paddi

Retrieve relevant documents for a query. Args: query: Search query top_k: Number of results to return

Retrieve documents using vector similarity search. Args: query: Search query text top_k: Number of results t

Perform hybrid retrieval. Args: query: Search query top_k: Number of results filters: Metadata f

Retrieve and rerank documents. Args: query: Search query top_k: Final number of results filters:

Generate query variations and aggregate results. Args: query: Original search query top_k: Number of results

TODO: Rollback to previous deployment

TODO: Rollback deployment to previous version Steps: 1. Get previous deployment revision 2. Execute rollback using kubectl or K8s AP

Root endpoint. Returns basic API information.

Root endpoint - API information TODO: Return API information Should include: - API name - Version - Status - Documentat

Run continuous health checks at specified interval. Args: interval_seconds: Interval between health checks TODO

TODO: Run complete validation suite Steps: 1. Run schema validation 2. Run completeness checks 3. Run range

Wrap stream with error handling and recovery. TODO: Implement safe streaming wrapper - Catch exceptions from generator - Send error

Sample text chunks for testing. TODO: Implement sample chunks - Create list of text chunks - Simulate chunking output Returns:

Sample documents for testing. TODO: Implement sample documents - Create list of test documents - Include various content types -

Sample embeddings for testing. TODO: Implement sample embeddings - Create dummy embedding vectors - Match expected dimension Re

Sample generation request. TODO: Implement sample request - Create GenerateRequest object - Use realistic parameters Returns:

TODO: Create sample PIL Image Returns: PIL Image (RGB, 224x224) Example implementation: return Image.new('RGB', (224, 2

TODO: Create sample test image Steps to implement: 1. Create a simple PIL Image (RGB) 2. Save to BytesIO 3. Return image bytes

Sample RAG request. TODO: Implement sample RAG request - Create RAGGenerateRequest object - Include retrieval parameters Return

TODO: Create sample input tensor Returns: PyTorch tensor with shape (1, 3, 224, 224) Example implementation: return tor

TODO: Save preprocessing artifacts (pipelines, encoders, scalers) Steps: 1. Create output directory 2. Save pipeline

Save experiment results to file. Args: experiment_name: Name of experiment results: Results dictionary output_dir: O

TODO: Save trained model to disk Args: output_path: Path to save model

TODO: Scale numerical features Steps: 1. Identify numerical columns 2. Apply scaling strategy 3. Save scaler

TODO: Send success notification for deployment Information to include: - Model version deployed - Deployment timestamp - Health

TODO: Send notification about deployment rollback Returns: bool: True if notification sent

TODO: Implement success notification (Slack, Email, etc.) Steps: 1. Gather pipeline execution summary 2. Format notification message

TODO: Set value in cache

Create test data

Set up test fixtures

Set number of active requests. TODO: Implement active request tracking - Set gauge to current count Args:

Configure CORS middleware. TODO: Implement CORS setup - Allow specified origins - Configure allowed methods - Set allowed header

TODO: Setup Great Expectations suite Steps: 1. Initialize Great Expectations context 2. Create or load expectation s

TODO: Setup logging configuration Steps to implement: 1. Create logger instance 2. Set log level (DEBUG, INFO, WARNING, ERROR) 3

TODO: Setup MLflow tracking Steps: 1. Set MLflow tracking URI 2. Create or get experiment 3. Enable autologg

Gracefully shutdown the LLM server. TODO: Implement cleanup: 1. Cancel any pending requests 2. Flush GPU memory

TODO: Cleanup on application shutdown Tasks: - Log shutdown message - Clean up model resources if needed - Close any open connec

TODO: Get current cache size

Skip model registration and log reason

Start metrics HTTP server. TODO: 1. Start Prometheus HTTP server 2. Log server start 3. Verify metrics endpo

Start continuous GPU monitoring. TODO: Implement continuous monitoring - Run update loop - Sleep between updates

TODO: Load ML model at application startup Steps to implement: 1. Log startup message 2. Load model from disk or model registry

Stop metrics HTTP server. TODO: 1. Gracefully shutdown server 2. Flush any pending metrics 3. Log shutdown

Stream multiple requests concurrently. TODO: Implement batch streaming - Process multiple requests in parallel - Multiplex results i

Stream LLM tokens as SSE events. This wraps the raw LLM generator and formats output as SSE. TODO: Implement streaming wrapper - It

Stream RAG-augmented generation. This performs retrieval first, then streams generation. TODO: Implement RAG streaming - Send "retr

Wrap generator with heartbeat events. Sends periodic heartbeat events to keep connection alive. Useful for preventing timeouts during sl

Clean up after tests

Create temporary directory with files. TODO: Implement temp directory fixture - Create directory structure - Add sample files -

Create temporary file for testing. TODO: Implement temp file fixture - Create temp file with content - Return path - Auto-cleanu

TODO: Test 404 response for non-existent endpoint Example implementation: response = client.get("/nonexistent") assert respo

TODO: Test API key auth - Request without key - Verify 401 response - Test with valid key

TODO: Test batch embedding - Generate batch embeddings - Verify efficiency - Check all embeddings

TODO: Test batch processing - Generate multiple completions - Verify batch efficiency - Check all outputs

TODO: Test batch indexing - Index documents - Verify upsert called - Check statistics

TODO: Test batch inference Steps to test: 1. Create batch of images 2. Run batch inference 3. Assert correct number of predictio

TODO: Test that batch inference is faster than sequential Steps to test: 1. Create batch of images 2. Time batch inference 3. Ti

TODO: Test categorical encoding

TODO: Test classification evaluation

FastAPI test client. TODO: Implement test client - Create FastAPI app instance - Add test routes - Return TestClient Return

TODO: Test completeness checks

TODO: Test handling concurrent prediction requests Steps to test: 1. Send multiple prediction requests concurrently 2. Assert all re

TODO: Test continuous batching - Process requests with batching - Verify throughput improvement - Check latency accep

TODO: Test CSV data fetching

TODO: Test database source with mocked connection