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High-performance ML inference service with Python bindings to Rust
Build a hybrid ML inference service where performance-critical components are written in Rust and exposed to Python via PyO3. Implements fast tokenization, tensor operations, and post-processing in Rust while using Python for model loading and orchestration. Features SIMD optimizations, memory pooling, and zero-copy serialization.
PyO3 bindings
Fast tokenization
SIMD tensor ops
Zero-copy serialization
Memory pooling
Parallel processing
ONNX inference
Dynamic batching
Model versioning
Performance metrics
Standard architecture
Rust
Python
PyO3
ML systems
Performance optimization
SIMD
Master PyO3 bindings
Optimize Rust-Python FFI
Build high-performance ML
Implement SIMD in Rust
Design zero-copy APIs
Profile mixed-language systems
PyO3 + maturin configuration
Fast tokenizer in Rust
ndarray tensor operations
Rayon parallel processing
Python bindings API
SIMD acceleration
Memory pooling strategy
Zero-copy serialization
FastAPI inference service
Dynamic batching