Shelf Blog: AI Deployment

As the deployment of Large Language Models (LLMs) continues to expand across sectors such as healthcare, banking, education, and retail, the need to understand and effectively evaluate their capabilities grows with each new application. Solid LLM evaluation metrics for assessing output quality are...

Generative AI has presented businesses with unprecedented access to data and the tools to mine that data. It’s tempting to see all data as beneficial, but the older-than-AI rule, Garbage In, Garbage Out, still applies. To truly understand the effectiveness and safety of GenAI in your...

As companies work to ensure the accuracy, compliance, and ethical alignment of their AI systems, they are increasingly recognizing the importance of AI audits in their governance toolkits.  What Is an AI Audit? An AI audit is a comprehensive examination of an AI system that scrutinizes its...

Machine learning pipelines automate and streamline the development, deployment, and maintenance of machine learning models. They ensure consistency, reduce manual effort, enhance scalability, and improve the reliability of your machine learning projects.  Ultimately, this automation...

Machine learning (ML) offers powerful tools for predictive analytics, automation, and decision-making. By analyzing vast amounts of data, ML models can uncover unique patterns and insights. This can drive efficiency, innovation, and competitive advantage for your organization. But, the true value...

Real-world AI systems rely heavily on human interactions to refine their capabilities. Embedding human feedback ensures these tools evolve through experiential learning. Regular, informed user feedback allows AI systems to self-correct and align more closely with user expectations. However,...