Best of NVIDIA — October 2024

  1. 1
    Video
    Avatar of youtubeYouTube·2y

    How to Learn AI and Get Certified by NVIDIA

    NVIDIA offers a range of AI courses, some free and some paid, to help you learn and get certified in AI. These courses cover various aspects of AI including generative AI, retrieval augmented generation, CUDA, deep learning, and prompt engineering. NVIDIA certifications can validate your skills and make you stand out in the job market. Learning paths are available for foundational AI skills and more advanced topics like large language models and transformers.

  2. 2
    Article
    Avatar of nvidiadevNVIDIA Developer·2y

    Creating RAG-Based Question-and-Answer LLM Workflows at NVIDIA

    NVIDIA has developed a new system architecture for question-and-answer workflows using retrieval-augmented generation (RAG). They found that users want more than just RAG-driven tasks, appreciating features like web search and summarization. By integrating Perplexity's search API, LlamaIndex, NVIDIA NIM microservices, and Chainlit, they created a versatile chat application. The post provides detailed instructions on setting up and deploying this system, highlighting the ease of development with NVIDIA's tools.

  3. 3
    Video
    Avatar of techlinkedTechLinked·2y

    This Is Gonna Be Good.

    Nvidia is anticipated to unveil its RTX 5090, 5080, and 5070 at CES in January 2025, boasting significant power and memory improvements. Intel has launched its Aero Lake series, which promises better energy efficiency compared to its predecessors. Tesla introduced its Cyber Cab and Optimus robots at their recent event, showcasing advancements in self-driving and robotics technology. The post also covers CookUnity meal service, a data breach at the Internet Archive, and Instagram's moderation issues.

  4. 4
    Article
    Avatar of nvidiadevNVIDIA Developer·2y

    Building AI Agents to Automate Software Test Case Creation

    NVIDIA's DriveOS team has developed Hephaestus (HEPH), a generative AI framework to automate the creation of software test cases. By leveraging large language models (LLMs), HEPH reduces the manual labor involved in test creation, making the process faster and more efficient. The framework handles everything from document traceability to generating and executing context-aware tests. The post highlights the potential of HEPH in saving time, improving test coverage, and supporting multiple input formats. Future enhancements include modularity and interactive feedback for greater flexibility and accuracy in test generation.

  5. 5
    Article
    Avatar of freecodecampfreeCodeCamp·2y

    Which Tools to Use for LLM-Powered Applications: LangChain vs LlamaIndex vs NIM

    Considering building an application with a Large Language Model? LangChain, LlamaIndex, and NVIDIA NIM offer unique features to help you. LangChain is versatile for developing applications with data-aware and agent-driven components. LlamaIndex excels in data indexing and retrieval, optimizing how large language models access and process information. NVIDIA NIM focuses on high-performance model deployment, offering scalable and secure solutions. Each tool's strengths cater to different aspects of LLM application development, making your choice dependent on your specific needs, be it flexible integration, efficient data handling, or fast and secure deployment.

  6. 6
    Article
    Avatar of nvidiadevNVIDIA Developer·2y

    Evaluating Medical RAG with NVIDIA AI Endpoints and Ragas

    Retrieval-augmented generation (RAG) is revolutionizing the medical field by combining large language models with external knowledge retrieval to provide accurate and contextually relevant information. This hybrid approach is particularly beneficial in drug discovery and clinical trial screening. However, evaluating RAG systems for medical applications poses unique challenges, including scalability, lack of benchmarks, and the need for domain-specific metrics. The post discusses using LangChain NVIDIA AI endpoints and the Ragas evaluation framework to address these challenges, with a detailed tutorial on setting up and evaluating medical RAG systems using a synthetic dataset.

See all NVIDIA archives