Shelf Blog: AI Deployment
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
Microsoft Copilot is a powerful AI assistant that helps you streamline tasks and boost your productivity. However, like all generative AI, it occasionally produces “hallucinations,” which are responses that sound confident but may be factually incorrect. In fact, some studies suggest that...
Microsoft Copilot is changing the game for teams looking to get more out of their data. But to really see its full potential, your data needs to be prepared thoughtfully—organized, clean, and secure. Without the right groundwork, you’re setting yourself up for spotty insights, unreliable...
Microsoft Copilot is a powerful tool, but like any AI, it can provide incorrect or misleading answers. To ensure you’re getting the most accurate responses, it’s essential to understand how to prompt Copilot properly in order to prevent bad outputs. Let’s explore how Microsoft...
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 (ML) systems often operate behind complex algorithms, leading to untraceable errors, unjustified decisions, and undetected biases. In the face of these issues, there is a shift towards using interpretable models that ensure transparency and reliability. This shift is crucial for...
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,...
The quality of your data can make or break your business decisions. Data cleaning, the process of detecting and correcting inaccuracies and inconsistencies in data, is essential for maintaining high-quality datasets. Clean data not only enhances the reliability of your analytics and business...
Fairness metrics are quantitative measures used to assess and mitigate bias in machine learning models. They help identify and quantify unfair treatment or discrimination against certain groups or individuals. As AI systems grow in influence, so does the risk of perpetuating or amplifying biases...