Explore the role of DevOps in establishing reliable AI data and governance frameworks, enhancing your organization's data integrity and operational success.
Table-augmented generation (TAG) and LOTUS bridge AI and databases, enabling complex queries using LLMs. They address the limits of Text2SQL and RAG models.
Utilizing AWS SageMaker and Glue to create a fraud detection system using ETL, deep learning, and XGBoost for scalable, efficient, and accurate results.
Automated bug fixing has evolved from simple template-based approaches to sophisticated AI systems powered by LLMs, agents, agentless, and RAG paradigms.
AI agents streamline workflows by autonomously processing claims, detecting fraud, ensuring compliance, and enhancing decision-making with real-time insights.
A data culture fosters data and AI use to improve decision-making, drive innovation, build trust, and ensure organizational success through collaboration.
In this article, learn about AI in agile product teams, gain insights from deep research, and explore what it means for your practice as an agile practitioner.
We will explore the importance of eXplanation in fraud detection models and learn how it can help to understand different patterns of fraud in our system.
Loss functions measure how wrong an AI's predictions are. Different loss functions are used for different types of problems (regression or classification).
Efficient multimodal data processing using GPU-accelerated pipelines, neural networks, and hybrid storage for scalable, low-latency AI-driven applications.
One of the essential security measures to address LLM security involves securing access to the LLMs so that only authorized individuals can access data.