Breaking Up With Flask & FastAPI: Why ML Model Serving Requires A Specialized Framework

Setting up the necessary machine learning infrastructure to run these big models is another challenge. We need a dedicated model server for running model inference (using frameworks like Triton oder vLLM), powerful GPUs to run everything robustly, and configurability in our servers to make sure they're high throughput and low latency. Tuning the in... See more

Data science teams can use Baseten to efficiently serve, integrate, design, and ship their custom machine learning models with ease. A key benefit of Baseten is that it collapses the innovation cycle for ML apps, resulting in cheaper experimentation and greater success. It unblocks ML efforts currently bottlenecked by infrastructure, frontend, and ... See more

This transformation begins with less-complex, one-off models (typically referred to as machine learning) to do simple tasks that are forgiving to mistakes; for example, Netflix using AI to recommend shows.