AI and platform engineering are emerging as revolutionary integrations for cloud-native environments, increasing scalability, reliability, and efficiency.
RAG is a powerful AI approach that uses real-time data retrieval to provide accurate, contextually appropriate responses, aiding in the development of AI applications.
This article sheds light on how artificial intelligence and cybersecurity converge to revolutionize threat detection, incident response, and vulnerability management.
Previously, we saw how LangChain provided an efficient and compact solution for integrating Ollama with SingleStore. But what if we were to remove LangChain?
This article will guide you on starting out with Retrieval-Augmented Generation with LLMs. You will learn what RAG is and how to implement it in your application.
Leverage WatsonX's AI capabilities to create innovative applications that streamline processes and boost productivity, making life easier and more productive for users.
Explore a Firebase project that uses the Gen AI Kit with Gemma using Ollama and learn how to test it locally with the Firebase emulator and the Gen UI Kit.
This article explains the inbuilt vector search functionality in Cosmos DB for MongoDB vCore and also provides a quick exploration guide using Python code.
Self-driving cars are becoming common; however, software development outside of ML topics is not well known. This post demystifies middleware for vehicles.
Learn how integrating Graph Neural Networks (GNNs) with the Branch & Bound (B&B) algorithm significantly advances solving the Traveling Salesman Problem (TSP).
What is a universal semantic layer, and how is it different from a semantic layer? Is there actual semantics involved? Who uses that, how, and what for?
AI Risk Management Framework guides developers to build AI systems by identifying and mitigating risks through testing, secure coding, and continuous monitoring.