Shelf Blog: AI Deployment

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...

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...

Output evaluation is the process through which the functionality and efficiency of AI-generated responses are rigorously assessed against a set of predefined criteria. It ensures that AI systems are not only technically proficient but also tailored to meet the nuanced demands of specific...

AI has revolutionized how we operate and make decisions. Its ability to analyze vast amounts of data and automate complex processes is fundamentally changing countless industries. However, the effectiveness of AI is deeply intertwined with the quality of data it processes. Poor data quality can...

Evaluating your organization’s tools, leadership, business model, and partnerships can identify areas of improvement needed to find success with your AI strategy. It’s not necessarily the case that AI will lead to automation of all business — and it may be your industry is one of the least-affected by this disruption — but these steps will ensure your organization is prepared for the future.

If you have been waiting for strong evidence before investing your organization’s time and resources into AI, the knowledge most organizations have already taken that step should be convincing. Now is the time to begin your journey into AI for your enterprise.