This article intends to highlight the importance of secure file transfer and its role in support of an organization's artificial intelligence (AI) initiatives.
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.
Quarkus enables a full CRUD API with just two classes using Hibernate ORM with Panache. No controllers or repositories needed — just define two classes, and deploy.
GenAI creates working software — faster than wireframes. Enter a prompt, you get a database, app, and API. With declarative business logic. Customize in your IDE.
Learn SRE best practices for Java applications to ensure high availability, performance, and scalability, covering monitoring, logging, security, and more.
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.