After pitching Warp Speed and knowing I would gain access to an 8 GPU inference server, I decided to go on a deep dive into using NVIDIA hardware and Inference Endpoints for GenAI applications.
This training provided hands-on experience in transformer architectures, retrieval-augmented generation (RAG), AI Agents, and sizing LLM inference systems, equipping me with the skills to design, optimize, and deploy AI-powered solutions at scale.
NVIDIA’s Generative AI and LLM Training, s a comprehensive program covering the latest advancements in deep learning, large language models (LLMs), and AI-driven applications.
This multi-course GenAI and LLM training program focused on developing, optimizing, and deploying generative AI and LLM-based systems with an emphasis on practical AI deployment.
I also gained expertise in NVIDIA NIM™, deep learning with PyTorch and building RAG agents and ML pipelines using LangChain.
Through hands-on projects, I applied these skills to develop AI-native applications, optimize inference pipelines, and integrate LLMs into real-world AI solutions.
LLM-Driven RAG System for Research Papers
• Built a retrieval-augmented generation (RAG) agent to enable AI-powered document analysis.
• Designed an embedding-based search system for semantic similarity and guard railing.
• Deployed a vector-based knowledge retrieval system, enabling efficient and context-aware responses.
Low-Latency AI Chatbot using NVIDIA NIM™
• Deployed an AI chatbot using NVIDIA NIM™ microservices for real-time AI model inference.
• Integrated LLM inference pipelines, optimizing response times for high-performance AI applications.
Building RAG Agents with LLMs
• Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components.
• Design a dialog management and document reasoning system that maintains state and coerces information into structured formats.
• Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing.
• Implement, modularize, and evaluate a RAG agent that can answer questions about the research papers in its dataset without any fine-tuning.
• Exploration of LLM inference interfaces and microservices.
• Designing LLM pipelines using LangChain, Gradio, and LangServe.
• Managing dialog states and integrating knowledge extraction.
• Strategies for working with long-form documents.
• Utilizing embeddings for semantic similarity and guardrailing.
• Implementing vector stores for efficient document retrieval.
• Deep Learning Fundamentals – Training and fine-tuning deep learning models using PyTorch and transfer learning techniques.
• Transformer-Based LLMs – Understanding text generation, text classification, summarization, named-entity recognition (NER), question answering and NLP model optimization.
• Retrieval-Augmented Generation (RAG) – Designing AI-powered knowledge retrieval systems for enhanced chatbot and search capabilities. We explored preparing data sets, splitting data into chunks, loading vector databases and retrieval strategies.
• Synthetic Data Generation – Went through an end-to-end development workflow for generating synthetic data using Transformers, including data preprocessing, model pre-training, fine-tuning, inference, and evaluation
• Sizing LLM Inference Systems - Learned about model optimization and deployment, covering optimizations like, prefill and decoding, latency and throughput trade-offs, tensor parallelism, in-flight batching. We also covered benchmarking, scaling strategies and optimization for total cost of ownership (TCO) for on-prem and external cloud deployment.
• AI Model Deployment & Optimization – Scaling multi-GPU AI workloads using NVIDIA NIM™ microservices and TensorRT-LLM.
After pitching Warp Speed and knowing I would gain access to an 8 GPU inference server, I decided to go on a deep dive into using NVIDIA hardware and Inference Endpoints for GenAI applications.
This training provided hands-on experience in transformer architectures, retrieval-augmented generation (RAG), AI Agents, and sizing LLM inference systems, equipping me with the skills to design, optimize, and deploy AI-powered solutions at scale.
NVIDIA’s Generative AI and LLM Training, s a comprehensive program covering the latest advancements in deep learning, large language models (LLMs), and AI-driven applications.
This multi-course GenAI and LLM training program focused on developing, optimizing, and deploying generative AI and LLM-based systems with an emphasis on practical AI deployment.
I also gained expertise in NVIDIA NIM™, deep learning with PyTorch and building RAG agents and ML pipelines using LangChain.
Through hands-on projects, I applied these skills to develop AI-native applications, optimize inference pipelines, and integrate LLMs into real-world AI solutions.
• Deep Learning Fundamentals – Training and fine-tuning deep learning models using PyTorch and transfer learning techniques.
• Transformer-Based LLMs – Understanding text generation, text classification, summarization, named-entity recognition (NER), question answering and NLP model optimization.
• Retrieval-Augmented Generation (RAG) – Designing AI-powered knowledge retrieval systems for enhanced chatbot and search capabilities. We explored preparing data sets, splitting data into chunks, loading vector databases and retrieval strategies.
• Synthetic Data Generation – Went through an end-to-end development workflow for generating synthetic data using Transformers, including data preprocessing, model pre-training, fine-tuning, inference, and evaluation
• Sizing LLM Inference Systems - Learned about model optimization and deployment, covering optimizations like, prefill and decoding, latency and throughput trade-offs, tensor parallelism, in-flight batching. We also covered benchmarking, scaling strategies and optimization for total cost of ownership (TCO) for on-prem and external cloud deployment.
• AI Model Deployment & Optimization – Scaling multi-GPU AI workloads using NVIDIA NIM™ microservices and TensorRT-LLM.
LLM-Driven RAG System for Research Papers
• Built a retrieval-augmented generation (RAG) agent to enable AI-powered document analysis.
• Designed an embedding-based search system for semantic similarity and guard railing.
• Deployed a vector-based knowledge retrieval system, enabling efficient and context-aware responses.
Low-Latency AI Chatbot using NVIDIA NIM™
• Deployed an AI chatbot using NVIDIA NIM™ microservices for real-time AI model inference.
• Integrated LLM inference pipelines, optimizing response times for high-performance AI applications.
Building RAG Agents with LLMs
• Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components.
• Design a dialog management and document reasoning system that maintains state and coerces information into structured formats.
• Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing.
• Implement, modularize, and evaluate a RAG agent that can answer questions about the research papers in its dataset without any fine-tuning.
• Exploration of LLM inference interfaces and microservices.
• Designing LLM pipelines using LangChain, Gradio, and LangServe.
• Managing dialog states and integrating knowledge extraction.
• Strategies for working with long-form documents.
• Utilizing embeddings for semantic similarity and guardrailing.
• Implementing vector stores for efficient document retrieval.
After pitching Warp Speed and knowing I would gain access to an 8 GPU inference server, I decided to go on a deep dive into using NVIDIA hardware and Inference Endpoints for GenAI applications.
This training provided hands-on experience in transformer architectures, retrieval-augmented generation (RAG), AI Agents, and sizing LLM inference systems, equipping me with the skills to design, optimize, and deploy AI-powered solutions at scale.
NVIDIA’s Generative AI and LLM Training, s a comprehensive program covering the latest advancements in deep learning, large language models (LLMs), and AI-driven applications.
This multi-course GenAI and LLM training program focused on developing, optimizing, and deploying generative AI and LLM-based systems with an emphasis on practical AI deployment.
I also gained expertise in NVIDIA NIM™, deep learning with PyTorch and building RAG agents and ML pipelines using LangChain.
Through hands-on projects, I applied these skills to develop AI-native applications, optimize inference pipelines, and integrate LLMs into real-world AI solutions.
• Deep Learning Fundamentals – Training and fine-tuning deep learning models using PyTorch and transfer learning techniques.
• Transformer-Based LLMs – Understanding text generation, text classification, summarization, named-entity recognition (NER), question answering and NLP model optimization.
• Retrieval-Augmented Generation (RAG) – Designing AI-powered knowledge retrieval systems for enhanced chatbot and search capabilities. We explored preparing data sets, splitting data into chunks, loading vector databases and retrieval strategies.
• Synthetic Data Generation – Went through an end-to-end development workflow for generating synthetic data using Transformers, including data preprocessing, model pre-training, fine-tuning, inference, and evaluation
• Sizing LLM Inference Systems - Learned about model optimization and deployment, covering optimizations like, prefill and decoding, latency and throughput trade-offs, tensor parallelism, in-flight batching. We also covered benchmarking, scaling strategies and optimization for total cost of ownership (TCO) for on-prem and external cloud deployment.
• AI Model Deployment & Optimization – Scaling multi-GPU AI workloads using NVIDIA NIM™ microservices and TensorRT-LLM.
LLM-Driven RAG System for Research Papers
• Built a retrieval-augmented generation (RAG) agent to enable AI-powered document analysis.
• Designed an embedding-based search system for semantic similarity and guard railing.
• Deployed a vector-based knowledge retrieval system, enabling efficient and context-aware responses.
Low-Latency AI Chatbot using NVIDIA NIM™
• Deployed an AI chatbot using NVIDIA NIM™ microservices for real-time AI model inference.
• Integrated LLM inference pipelines, optimizing response times for high-performance AI applications.
Building RAG Agents with LLMs
• Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components.
• Design a dialog management and document reasoning system that maintains state and coerces information into structured formats.
• Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing.
• Implement, modularize, and evaluate a RAG agent that can answer questions about the research papers in its dataset without any fine-tuning.
• Exploration of LLM inference interfaces and microservices.
• Designing LLM pipelines using LangChain, Gradio, and LangServe.
• Managing dialog states and integrating knowledge extraction.
• Strategies for working with long-form documents.
• Utilizing embeddings for semantic similarity and guardrailing.
• Implementing vector stores for efficient document retrieval.
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