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You are an ML engineer at a startup building a recommendation engine for an e-commerce platform. Training jobs on large datasets are sporadic but compute-intensive, while the inference endpoint must handle variable traffic throughout the day. The company is cost-conscious and requires a solution that balances cost efficiency, scalability, and performance. Which resource allocation approach is MOST SUITABLE for training and inference, and why?