Shelf Blog: AI Deployment
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
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...
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...
Successful AI projects require more than just cutting-edge technology. They demand a clear vision, robust data governance, ethical considerations, and an adaptive organizational culture. In this article, we delve into the common pitfalls that can derail AI projects. We also offer insights and...
Hallucinations and ungrounded results are a significant challenge in Content Processing systems. When AI-generated content contains statements that are inconsistent with the input data or knowledge base, it can lead to the spread of misinformation and erode trust in the system. Microsoft Azure’s...
Subject Matter Experts (SMEs) are the architects of quality and precision in AI development. But how can you be the best SME for your organization’s AI output review initiatives? SMEs are presented with a great responsibility – to identify discrepancies, biases, and areas for potential...