In this episode, we discuss using AWS Lambda for machine learning inference. We cover the tradeoffs between GPUs and CPUs for ML, tools like ggml and llama.cpp for running models on CPUs, and share examples where we've experimented with Lambda for ML like podcast transcription, medical imaging, and natural language processing. While Lambda ML is still quite experimental, it can be a viable option for certain use cases.

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In this episode, we mentioned the following resources.

• Episode "46. How do you do machine learning on AWS?": https://awsbites.com/46-how-do-you-do-machine-learning-on-aws/


• Episode "108. How to Solve Lambda Python Cold Starts": https://awsbites.com/108-how-to-solve-lambda-python-cold-starts/


• ggml (the framework): https://github.com/ggerganov/ggml


• ggml (the company): https://ggml.ai


• llama.cpp: https://github.com/ggerganov/llama.cpp


• whisper.cpp: https://github.com/ggerganov/whisper.cpp


• whisper.cpp WebAssembly demo: https://whisper.ggerganov.com/


• ONNX Runtime: https://onnxruntime.ai/


• An example of using whisper.cpp with the Rust bindings: https://github.com/lmammino/whisper-rs-example


• Project running Whisper.cpp in a Lambda function: https://github.com/eoinsha/whisper_lambda_cpp


• AWS Lambda Image Container Chest X-Ray Example: https://github.com/fourTheorem/lambda-image-cxr-detection


• Episode "103. Building GenAI Features with Bedrock": https://awsbites.com/103-building-genai-features-with-bedrock/⁠

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