Stellar inference speed via AutoNAS
Practical AI
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Optimizing Inference in Machine Learning
This chapter explores the significance of inference in the machine learning lifecycle, focusing on the challenges and considerations in deploying models at scale. It addresses operational factors such as latency, throughput, and optimal hardware, particularly in real-time applications like self-driving cars. The discussion advocates for a holistic approach to model development that includes inference requirements from the outset to ensure successful deployment.
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