OpenAI DevOps Engineer System Design Questions

OpenAI frequently trains and fine-tunes machine learning models, requiring an organized API to serve these requests efficiently.
Problem Statement: Design a class named ModelTrainingAPI that allows clients to submit training jobs, check their progress, and retrieve results once completed.

• submit_training_job(model_id: str, data: Any) -> str: Submit a new training job and return the job ID.


• check_job_status(job_id: str) -> str: Retrieve the current status of a training job.


• get_job_result(job_id: str) -> Any: Retrieve the results of a completed job.

Example 1:
Input: Submit a training job with a model ID and training data.
Output: Returns a unique job ID to track.
Explanation: The API facilitates the model training workflow by providing necessary actions.
Constraints:

• The API should handle up to 500 concurrent training jobs.


• Training jobs should support data sizes up to 10GB.

OpenAI's microservices architecture requires a centralized configuration management system to ensure consistent settings across various services.
Problem Statement: Design a class named ConfigManager, which handles loading and retrieving configurations from a key-value store. The class should support watching for configuration changes and notify services when updates occur.

• load_config() - Load configurations from a given source.


• get_config(key: str) -> str - Retrieve the value for the provided key.


• watch_config(key: str, callback: Callable) - Register a callback to be called when the configuration for the specified key changes.

Example 1:
Input: Load configuration from a JSON file.
Output: Key-value pairs are stored internally.
Explanation: The ConfigManager can load configurations on startup and provide them to other services.
Constraints:

• The key store should support at least 1000 key-value pairs.


• Must provide thread-safe access to configuration values.