What level of PyTorch expertise do I need for building a basic recommendation system for my e-commerce site?
You'll need intermediate-level expertise - someone who can work with custom datasets, implement collaborative filtering or content-based models, and handle the data preprocessing pipeline. Basic tutorial knowledge isn't sufficient for production systems that need to handle real customer data and scale properly.
How can I tell if a candidate actually knows PyTorch well versus just listing it on their resume?
Ask them to walk through a complete project they've built, from data preprocessing to deployment. Look for specific technical details about challenges they faced, optimization techniques they used, and how they measured success. A real expert can explain trade-offs and alternative approaches, not just what they implemented.
Can PyTorch handle real-time predictions, or is it just for training models?
PyTorch absolutely handles real-time inference and is increasingly used in production systems. However, deployment requires additional considerations like model optimization, serving infrastructure, and monitoring. The framework itself is just one piece of a complete production AI system.
How does PyTorch work with our existing tech stack if we're using AWS, PostgreSQL, and React?
PyTorch integrates well with modern tech stacks. You can deploy models using AWS SageMaker or containerized services, pull training data from PostgreSQL, and serve predictions via APIs that your React frontend can consume. The key is having someone who understands both PyTorch and system architecture.
How long does it typically take to build and deploy a PyTorch model for business use?
Simple models can be prototyped in 2-4 weeks, but production-ready systems typically take 2-4 months including data preparation, model development, testing, and deployment infrastructure. Complex projects like computer vision or NLP systems can take 6+ months depending on requirements and data availability.
What ongoing maintenance is needed after a PyTorch model is deployed?
Models need regular monitoring for performance degradation, periodic retraining with new data, and updates as business requirements change. Expect to allocate 20-30% of the original development effort annually for maintenance, monitoring, and improvements. This isn't a set-it-and-forget-it technology.
