Run:ai (Acquired by NVIDIA)’s Post

Deploying large language models (LLMs) on AWS can feel complex, but with thoughtful planning, it’s possible to achieve both efficiency and scalability. Based on Ishango.ai's experience, here are 5 practical tips to guide you in making informed decisions about your deployment. Let us know in the comments if these are helpful, and please share your tips 👇 👇 Naftali Indongo Shadrack Darku Mahamat Azibert Abdelwahab Zenas Awuku Chris Toumping Fotso Vincent Aduuna Frimpong Sharleen Muoki Cyrille Feudjio Opanin Adu Agyei Luel Hagos Beyene Nana Yaw Agyeman Stephen Adjignon Jan Ravnik Eunice Baguma Ball Oliver Angelil Chih-Chun Chen Cynthia Mbah Courage Seyram Wemegah

Don't miss the hashtag #AWSMarketplace and Caylent webinar on building an effective Large Language Model Operations (LLMOps) strategy. 👉 https://go.aws/4dQRMmT We will cover: ✅ How to develop a comprehensive LLMOps approach to simplify workload management & enable adaptability. ✅ Best practices for data prep, retrieval, & prompt engineering to optimize model performance. ✅ How to enable real-time monitoring to continuously assess metrics like response quality, cost, & time.

🚀 Excited to share my latest project! 🚀 I've developed a robust chat with PDF functionality using AWS services including S3, Lambda, OpenSearch, and Bedrock. 🗂️💬 Here's a quick overview: 🔹 𝗔𝗪𝗦 𝗦𝟯: Secure and scalable storage for all PDF files.  🔹 𝗔𝗪𝗦 𝗟𝗮𝗺𝗯𝗱𝗮: Automatically syncs the OpenSearch DB on document upload and delete, while also handling backend logic and PDF processing.  🔹 𝗔𝗪𝗦 𝗢𝗽𝗲𝗻𝗦𝗲𝗮𝗿𝗰𝗵: Powerful search and analytics capabilities to quickly locate information within PDFs, used as a vector DB.  🔹 𝗔𝗪𝗦 𝗕𝗲𝗱𝗿𝗼𝗰𝗸: Advanced features for seamless integration and enhanced functionality, utilizing amazon.titan-embed-text-v2 for text embedding. 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • Scalability: Designed to handle large volumes of PDFs without performance issues. • Efficiency: Fast and efficient data processing and retrieval. • Security: Ensures data privacy and integrity with AWS's robust security features. 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗲𝘁𝗮𝗶𝗹𝘀: RAG Implementation: Leveraged to enhance the chat functionality by combining the retrieval of relevant document passages with the power of generative AI for more accurate and informative responses. 𝗟𝗟𝗠 𝗠𝗼𝗱𝗲𝗹: meta.llama3-8b from Bedrock runtime. Excited to see the impact this can bring! 🚀 #AWS #Serverless #CloudComputing #ProjectManagement #PDFChat #TechInnovation #OpenSearch #S3 #Lambda #Bedrock #RAG

"Building Efficient Language Model Pipelines for Competitive Product Analysis with Azure and LangChain" Explore how to build robust language model pipelines with memory using Azure and LangChain, as detailed in Microsoft Learn's latest article. This architecture leverages Azure OpenAI Service models and the open-source LangChain framework to gather, analyze, and summarize information quickly and efficiently. ✅ SAR Software Inc. can utilize this architecture to develop a project focused on competitive product analysis. By integrating internal company product information with external data from web searches, SAR Software Inc. can create detailed, real-time reports that enhance their competitive edge and decision-making processes. Learn more about our expertise: https://lnkd.in/dYPm37jv  Learn more on this :  https://lnkd.in/dFAgjrMm

🚀 Unlock the Power of Precise Information Retrieval! 🚀 In our latest video, we guide you step-by-step on how to leverage the Elastic Search AI Platform to enhance retrieval specificity and ensure comprehensive answers to your user queries. 📄 Split your documents into passages 📈 Index these passages into Elasticsearch 🤖 Leverage RAG and LLMs to answer questions from your indexed data. Elevate your search and query response capabilities—watch now and transform your approach with the easy-to-use Search AI platform that helps everyone find what they need faster! #Elasticsearch #rag #GenAI

"Building Efficient Language Model Pipelines for Competitive Product Analysis with Azure and LangChain" Explore how to build robust language model pipelines with memory using Azure and LangChain, as detailed in Microsoft Learn's latest article. This architecture leverages Azure OpenAI Service models and the open-source LangChain framework to gather, analyze, and summarize information quickly and efficiently. ✅ SAR Software Inc. can utilize this architecture to develop a project focused on competitive product analysis. By integrating internal company product information with external data from web searches, SAR Software Inc. can create detailed, real-time reports that enhance their competitive edge and decision-making processes. Learn more about our expertise: https://lnkd.in/db_Mzi96  Learn more on this :  https://lnkd.in/detb5DA2

🛠️ 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗠𝘆 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗟𝗮𝗯 𝘄𝗶𝘁𝗵 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 & 𝗔𝗜 The Data Engineering Zoomcamp recommended to use GCP for a flexible development environment. Makes sense—just SSH in from anywhere! But Google only offers €280 for 90 days. Not ideal for long-term learning. So, I thought—why not use Terraform to automate infrastructure setup? This way, when funds (or time) run out, I can simply recreate my cloud environment. As a 𝙏𝙚𝙧𝙧𝙖𝙛𝙤𝙧𝙢 newbie, I turned to 𝘿𝙚𝙚𝙥𝙎𝙚𝙚𝙠-𝙍1-𝘿𝙞𝙨𝙩𝙞𝙡𝙡-𝙌𝙬𝙚𝙣-7𝘽 (𝘓𝘔 𝘚𝘵𝘶𝘥𝘪𝘰), running surprisingly well on my Mac. 🚀 𝗠𝗲𝗺𝗼𝗿𝘆 𝗨𝘀𝗮𝗴𝗲: 9.36𝘎 / 16.0𝘎 𝗣𝗿𝗼𝗺𝗽𝘁: Use Terraform to manage a Google Compute Engine instance and a Cloud Storage bucket with the following behavior:  1. First '𝙩𝙚𝙧𝙧𝙖𝙛𝙤𝙧𝙢 𝙖𝙥𝙥𝙡𝙮', both the compute engine and storage bucket     should be created.  2. On '𝙩𝙚𝙧𝙧𝙖𝙛𝙤𝙧𝙢 𝙙𝙚𝙨𝙩𝙧𝙤𝙮', the storage bucket should be destroyed, but     the compute engine should only be stopped (not destroyed).  3. On a subsequent '𝙩𝙚𝙧𝙧𝙖𝙛𝙤𝙧𝙢 𝙖𝙥𝙥𝙡𝙮', the compute engine should start     (not recreate), and the storage bucket should be recreated. - While I'm unsure if it cracked the problem, its 𝟰𝟯.𝟲𝟬 𝙨𝙚𝙘𝙤𝙣𝙙𝙨 𝙤𝙛 𝙩𝙝𝙞𝙣𝙠𝙞𝙣𝙜 sparked a ton of ideas. This is exactly how I imagine AI shaping the future of software development and system design—helping us explore, not just spit out the answer. I've attached its thought process for those who find it intriguing. #DataEngineering #Terraform #AI #GCP #DeepSeek #LMStudio

The latest update for #Elastic includes "Introducing Elastic's #OpenTelemetry Distribution for #Nodejs" and "Getting started with the Elastic #AI Assistant for #Observability and Amazon Bedrock". #Logging #Elasticsearch #DevOps https://lnkd.in/d3SsUnZ

The latest update for #Logzio includes "#AI-Powered #Observability: Picking Up Where #AIOps Failed" and "Once Again, Logz.io is an Observability Visionary". #Logging #DevOps https://lnkd.in/dGcUEWi

🚀 Bridging the Gap Between AI and Real-World Applications! 🌍💡 We are thrilled to unveil our latest project: Sentiment Analysis Model - Deployed Using AWS Services & Hugging Face! In this dynamic era, where customer feedback and sentiment shape the success of businesses, understanding emotions in text has become a game-changer. Here's what makes this project stand out: ✨ Key Features: 1. Cutting-Edge Technology: Leveraging the power of Machine Learning, Deep Learning, and Hugging Face Transformers. 2. Interactive Deployment: Deployed via Streamlit for a seamless user interface, accessible anytime, anywhere. 3. Scalable & Reliable: Hosted on AWS, ensuring high availability and performance. 4. Business-Driven Insights: Analyzing text sentiment for smarter decision-making and enhanced customer experience. 💻 Key Domains Covered: 1. AIOps & MLOps 2. Model Evaluation, Optimization, and Streamlined Deployment This project reflects our commitment to pushing the boundaries of AI, blending technical rigor with creativity to build impactful solutions. 🌟 Explore Our Work: 📂 GitHub Repository: [ https://lnkd.in/gUusGMWD ] We would love to hear your thoughts and suggestions! Let’s connect and collaborate to redefine innovation in AI together. #MachineLearning #DeepLearning #SentimentAnalysis #AI #HuggingFace #AWS #DataScience #Streamlit #AIOps #MLOps #Innovation