SKU/Artículo: AMZ-B0G2PQ1139

FINE-TUNING LLMS FOR REAL-WORLD AI SYSTEMS: PRACTICAL TECHNIQUES FOR LLMS FINE-TUNING, PARAMETER-EFFICIENT TRAINING, LORA, QLORA, RAG OPTIMIZATION, AND DEPLOYING CUSTOM MODELS AT SCALE

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Fuera de stock

Peso con empaque:
0.33 kg

Devolución:
No

Condición
Nuevo

Producto de:
Amazon

Sobre este producto

Comprehensive Fine-Tuning Strategies: Learn supervised fine-tuning, instruction-tuning, and domain adaptation to make LLMs excel at your specific tasks.
Parameter-Efficient Training: Apply LoRA, QLoRA, and PEFT methods to train powerful models with minimal compute and cost.
Advanced Dataset Engineering: Curate, augment, and optimize datasets to maximize model performance and reliability.
RAG and Retrieval Optimization: Integrate your fine-tuned LLMs with retrieval-augmented generation pipelines to minimize hallucinations and boost factual accuracy.
Deployment at Scale: Learn to serve models efficiently, implement monitoring and logging, and optimize inference for real-world applications.
Production-Ready Workflows: From experiment tracking to CI/CD pipelines, understand how professional AI teams operationalize LLM systems.
AI engineers and developers building real-world NLP and LLM applications.
Data scientists seeking to specialize in production-ready LLM fine-tuning.
Technical founders and product teams deploying custom AI solutions.
Anyone serious about mastering modern LLM optimization techniques and building scalable, reliable AI systems.

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FINE-TUNING LLMS FOR REAL-WORLD AI SYSTEMS: PRACTICAL TECHNIQUES FOR LLMS FINE-TUNING, PARAMETER-EFFICIENT TRAINING, LORA, QLORA, RAG OPTIMIZATION, AND DEPLOYING CUSTOM MODELS AT SCALE

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