SKU/Artículo: AMZ-B0FWPQVT1B
Parallel AI Programming in Python: Build Supercharged ML Workflows That Perform in Production
Format:
Kindle
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Fuera de stock
Fuera de stock
Peso con empaque:
0.76 kg
0.76 kg
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Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Parallel AI Programming in Python: Build Supercharged ML Workflows That Perform in Production. Are you wrestling with slow model training, stalled data pipelines, or unpredictable inference performance? You’re not alone—and you don’t have to accept sluggish results as the norm.Parallel AI Programming in Python offers the definitive, hands-on guide to turbocharging your machine learning workflows. From multicore CPU tricks to multi-GPU strategies and distributed architectures, this book equips you with the proven, production-ready techniques that top AI teams use every day.Inside, you’ll discover how toLeverage Python’s threading and multiprocessing to blast past the Global Interpreter LockBuild high-throughput I/O pipelines with asyncio, Dask, and Ray for lightning-fast data ingestionMaster GPU parallelism with PyTorch DDP, NCCL tuning, and mixed-precision trainingScale across clusters using MPI, Ray, and Dask—and know exactly when adding nodes stops delivering gainsOptimize numeric kernels with NumPy, Numba, Cython, and native extensions for peak performanceImplement real-time, fault-tolerant pipelines with Kafka/Pulsar, backpressure, and exactly-once semanticsProfile, benchmark, and tune your code with cProfile, py-spy, perf, and NVIDIA Nsight to fix bottlenecks fastWhen you put this book into practice, you willCut training times from days to hours using multi-GPU and distributed training patternsArchitect data pipelines that process millions of records per second without dropping a messageDeploy inference services that scale horizontally and maintain sub-100ms latency under heavy loadDetect and remedy performance pitfalls—from memory thrashing to straggler tasks—before they hit productionMaintain rock-solid environments with containerized setups, dependency pinning, and reproducible scriptsWhether you’re an ML engineer, data scientist, or infrastructure developer, Parallel AI Programming in Python delivers hands-on labs, clear code examples, and concise checklists to transform sluggish prototypes into production-grade systems.Take control of your AI pipeline performance today—add this essential resource to your toolkit and watch your Python workflows surge to new speeds.
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