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. 💰 SPONSORS 💰 AWS Bites is brought to you by fourTheorem, an Advanced AWS Partner. If you are moving to AWS or need a partner to help you go faster, check us out at fourtheorem.com ! 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/⁠ Do you have any AWS questions you would like us to address? Leave a comment here or connect with us on X, formerly Twitter: - ⁠⁠⁠⁠https://twitter.com/eoins⁠⁠⁠⁠ - ⁠⁠⁠⁠https://twitter.com/loige⁠⁠