9798272845498 - enterprise llm development with nvidia nemo framework: train, fine-tune, and deploy custom models with lora, nemo curator, and distributed gpu acceleration at scale di tanaka, caleb (9 risultati)
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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EUR 30,34
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
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Condizione: As New. Unread book in perfect condition.
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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EUR 29,96
Spedizione gratuitaSpedito in U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. Build and ship enterprise LLMs with NVIDIA NeMo, from clean data to fast, reliable deployment.Enterprises need custom models that respect governance, run efficiently on GPUs, and integrate with existing platforms. Generic tutorials stop short of what production teams require, leaving gaps i…n data quality, alignment, serving, and observability.This book provides a complete, field tested workflow using NeMo Curator, NeMo training and adapters, NeMo Retriever, NeMo Aligner, TensorRT LLM, Triton, and NIM. You get realistic configurations, scalable patterns, and evaluation practices that hold up in production.Design data pipelines with NeMo Curator for language ID, PII redaction, quality scoring, and multi stage deduplicationAssemble domain specific corpora for continued pretraining and supervised instruction tuningChoose and adapt foundation models, apply LoRA and prompt learning, and export clean adaptersStand up retrieval augmented generation with NeMo Retriever and vector search integrationAlign models with RLHF, reward modeling, and DPO while enforcing safety policiesQuantize and optimize with TensorRT LLM, then serve on Triton or NIM with OpenAI compatible endpointsRun distributed training with data, tensor, and pipeline parallelism plus AMP and robust checkpointingDeploy on Kubernetes with batching, autoscaling, and GPU scheduling tuned for throughput and latencyMonitor tokens, latency, and quality, run A B tests, version models, and manage cost for high volume trafficBuild multimodal and speech systems with NeMo ASR and TTS, and deliver real enterprise use cases end to endThis is a code heavy guide. Working Python, YAML, JSON, and Shell examples show end to end projects you can adapt directly to your stack.Grab your copy today and deliver enterprise ready LLMs with confidence. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Da: California Books, Miami, FL, U.S.A.California Books
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EUR 30,05
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Condizione: New. Print on Demand.
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Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 34,23
EUR 43,17 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. Build and ship enterprise LLMs with NVIDIA NeMo, from clean data to fast, reliable deployment.Enterprises need custom models that respect governance, run efficiently on GPUs, and integrate with existing platforms. Generic tutorials stop short of what production teams require, leaving gaps i…n data quality, alignment, serving, and observability.This book provides a complete, field tested workflow using NeMo Curator, NeMo training and adapters, NeMo Retriever, NeMo Aligner, TensorRT LLM, Triton, and NIM. You get realistic configurations, scalable patterns, and evaluation practices that hold up in production.Design data pipelines with NeMo Curator for language ID, PII redaction, quality scoring, and multi stage deduplicationAssemble domain specific corpora for continued pretraining and supervised instruction tuningChoose and adapt foundation models, apply LoRA and prompt learning, and export clean adaptersStand up retrieval augmented generation with NeMo Retriever and vector search integrationAlign models with RLHF, reward modeling, and DPO while enforcing safety policiesQuantize and optimize with TensorRT LLM, then serve on Triton or NIM with OpenAI compatible endpointsRun distributed training with data, tensor, and pipeline parallelism plus AMP and robust checkpointingDeploy on Kubernetes with batching, autoscaling, and GPU scheduling tuned for throughput and latencyMonitor tokens, latency, and quality, run A B tests, version models, and manage cost for high volume trafficBuild multimodal and speech systems with NeMo ASR and TTS, and deliver real enterprise use cases end to endThis is a code heavy guide. Working Python, YAML, JSON, and Shell examples show end to end projects you can adapt directly to your stack.Grab your copy today and deliver enterprise ready LLMs with confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
