Install CUDA on AWS GPU instances, containerize your deep learning model, and scale with ECS/EKS for cost-effective, high-performance training and inference.
Learn how MM-RAG revolutionizes smart city management by integrating text, images, and IoT data to deliver real-time actionable insights for urban challenges.
Deploy DeepSeek-R1 on Kubernetes using Ollama for inference and Open WebUI for seamless interaction. Supports local setups like KIND or cloud deployment.
vLLM is an open-source tool that helps with hosting AI models using an efficient inference engine. It can be used to power LLMs in real products and services.
Build a chat history implementation with Azure Cosmos DB for NoSQL Go SDK and LangChainGo, enhancing LLM context and enabling efficient testing with Testcontainers.
LLMs simplify time series forecasting by handling messy data and context. Combined with stats, they cut errors by 31%, delivering better, easier forecasts.
LLMs transform ETL with schema-less extraction, adaptive transformations, and multi-modal support, enabling scalable, efficient, and accessible data workflows.
This article provides a framework for architects and development teams on how to make wise decisions on building AI powered solutions for business problems.
Floyd’s Cycle Algorithm detects cyclic patterns in graphs to help identify fraudulent transaction loops in financial systems and prevent money laundering.