In the age of AI and Machine Learning, distributed systems are no longer optional—they are the backbone of every production-grade AI pipeline. Training jobs, feature stores, model serving, and data ingestion span multiple services across clusters, and every single component is a potential failure point. A timeout in the feature store can stall an entire training job. A network hiccup while calling an external LLM API can break your chatbot. A retry storm from hundreds of microservices can overwhelm your model serving infrastructure. Without proper timeout and retry strategies, these failures cascade, wasting compute hours, breaking SLAs, and frustrating users. The rise of Generative AI has made this even more critical. Every call to an external model API is a network call that can fail, and implementing exponential backoff with jitter is not just a best practice—it is a survival skill for building reliable AI applications. Distributed training adds another layer of complexity, where a single slow node or misconfigured timeout can stall weeks of training and waste millions in compute resources. Beyond the technical imperative, there is a career imperative. The industry is moving from a world where understanding algorithms was enough, to a world where understanding distributed systems is the differentiator. Mastering resilience patterns like timeouts and retries is what separates engineers who build toys from engineers who build systems that change the world. In short, AI is distributed, and distributed systems fail. Learning how to handle those failures gracefully is the foundation of production-grade AI.