Deploying & serving optimized ML pipelines using graalvm
summary
Claims that GraalVM plus Eclipse Deeplearning4j and JavaCPP compiles an entire ML pipeline — Python preprocessing, optimized operators, and native math libraries — into a single native binary that runs AOT and targets CPU, NVIDIA, ARM and specialty chips. The build produces a shared library with a Python wheel, supports zero-copy numpy interop, applies quantization/graph optimizations and deep‑learning compilers (TensorRT, TVM) and yields practical binaries of about 500MB–1GB while matching or improving Python runtime latency.
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chapters (7)
- 0:00Introduction and scope
- 3:12ML ops and pipeline components
- 6:15Hardware and performance challenges
- 10:38Pipeline architecture and data
- 16:28Optimization techniques
- 21:41Tools for model acceleration
- 24:38Production deployment strategies
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