Showing tag results for Machine Learning

This blog post introduces a comprehensive evaluation framework for enterprise chatbots powered by large language models (LLMs), specifically addressing the challenges of assessing Line of Business (LOB) agents in business-critical environments. The authors tackle the fundamental problem that traditional chatbot evaluation metrics fail to capture th...

Exploring how to efficiently run a RAG pipeline with structured language models (SLMs) and guardrails on Windows, achieving inference under 5 seconds with ONNX Runtime GenAI.

In this article, we delve into techniques for extracting valuable insights from customer feedback using Large Language Models (LLMs). By identifying themes, sentiment, and competitor comparisons from feedback, businesses can gain a competitive edge.

This post discusses a pattern to prevent a class of prompt injection attacks in LLM-based solutions. It emphasizes the importance of building strong foundational patterns to mitigate risks and avoid potential pitfalls. By implementing this pattern, teams can enhance the security of their tool-based solutions.

Journey on how to solve engineering document digitization problem through a proposed method and the end-to-end solution.

In this post we will describe how we were able to run our Dask-based data preparation at scale on Azure ML Compute Clusters, using Dask-MPI and the native Azure ML MPI support.

Media companies capture footage filmed for the entire day in what's known as ‘digital dailies’. When talking about terabytes and petabytes of content, storage costs can be a factor. Lets explore Machine Learning approaches to identify which content can be archived or discarded which will save on those storage costs.

Deep learning algorithms capable of learning and predicting customer behavior are allowing businesses to intervene with the right retention offers at the right time. CSE recently partnered with Majid Al Futtaim Ventures (MAF) to design and deploy a machine learning solution to predict attrition.