C++ Machine Learning: Turbocharge AI Workflows with High-Performance Training, On-Device Inference & Low-Level Tuning

By Richard M. Ponds

C++ Machine Learning: Turbocharge AI Workflows with High-Performance Training, On-Device Inference & Low-Level Tuning is your definitive guide for building end-to-end AI systems that marry the raw speed of C++ with the flexibility of modern ML. From orchestrating massive distributed training jobs to squeezing deep learning models onto microcontrollers, you'll master every layer-software, hardware, and tooling-to deliver blazing-fast, production-ready solutions.

What You'll Learn

✔ High-Performance Training

- Leverage C++ tensor libraries and CUDA/cuDNN integrations to implement custom neural network kernels.

- Scale across multi-GPU clusters with MPI, NCCL, and asynchronous pipelines for maximum throughput.

- Build distributed data loaders, sharded optimizers, and gradient accumulation schemes to handle billion-parameter models.

✔ On-Device Inference

- Embed optimized runtimes (ONNX Runtime, TensorRT, TVM) directly into your C++ applications.

- Exploit quantization (INT8/4-bit), pruning, and graph fusion to cut latency and memory footprint.

- Use SIMD/NEON intrinsics and custom microkernels to achieve real-time inference on CPUs and edge accelerators.

✔ Low-Level Tuning & Profiling

- Apply loop unrolling, cache blocking, and prefetch directives to maximize data locality.

- Harness advanced allocators, memory pools, and lock-free buffers for predictable performance under load.

- Profile end-to-end pipelines with Intel VTune, Linux perf, and custom tracers to pinpoint and eliminate bottlenecks.

✔ Bridging C++ with Python and DevOps

- Integrate C++ inference libraries with Python front-ends via Pybind11 and custom bindings.

- Automate CI/CD pipelines for continuous benchmarking, cross-compilation, and firmware updates.

- Embed unit tests and fuzzing harnesses to ensure robustness across hardware generations.

Who This Book Is For

- Machine learning engineers who need maximum performance and resource control.

- C++ developers transitioning into AI and data science domains.

- Embedded and IoT architects deploying vision, speech, or control models on constrained devices.

- Infrastructure teams building scalable training clusters, inference microservices, or hybrid CPU/GPU/FPGA platforms.

With hands-on examples, real-world case studies, and complete code listings, C++ Machine Learning arms you with the patterns, tools, and confidence to push AI from prototype to production-on any scale and in any environment.

• Format: Paperback
• Language: English
• ISBN: B0FPRJ9KVV
• ISBN13: 9798263734336
• Release Date: September 2025
• Publisher: Independently Published
• Length: 166 Pages
• Weight: 0.60 lbs.
• Dimensions: 0.3" x 6.7" x 9.6"