From the course: Applied AI for Human Resources
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Model serving best practices - Python Tutorial
From the course: Applied AI for Human Resources
Model serving best practices
- One of the most critical yet overlooked area of machine learning is model serving, the ability to serve models at scale and yet in cost-effective ways is a key success factor for AA projects. Today, GPUs provide multifold processing power for machine learning, however, they are most useful in model training activities. They may or may not provide a significant advantage in model serving. It's highly recommended to compare costs between CPU and GPU implementations for serving as using 10 CPUs instead of one GPU maybe cost-effective overall. Use a model serving platform like TensorFlow Serving to harness out of the box functionality and scaling. It is important to track model inference or prediction performance overtime. Both accuracy and speed of prediction should be tracked to ensure that they continue to perform as the sayer. Model drift happens when models lose accuracy due to new types of real time data. Model…
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