This post presents a hierarchical waterfall framework for evaluating query classification, retrieval, and generation in multi-agent LLM systems.
data science and software engineering teams can choose the best tools for their respective roles in delivering machine learning models to production.
How to manage multiple independent python projects, with different dependencies, inside Visual Studio Code.
This blog post explores the development and evaluation of an AI assistant that converts natural language into robotic code.
Azure Machine Learning is known for training and deploying models, but can also be used for running experiments. This blog post will show us how we have implemented our Evaluation platform on Azure Machine Learning.
Potential semantic routing solution using Azure AI Search for scalable query handling.
Solution of store inventory allocation problem of CPG
Our team used AI tools to write code, documentation, indexes, etc. extensively during our last engagement. This post will talk about what we used and how we used it.
Movies and TV shows require multiple takes per scene and may have a substantial amount of archival footage as a result. Here, we use Azure Cognitive Services and custom code to develop a multi-model Machine Learning (ML) solution to automatically detect discardable footage to save media companies manual archiving hours and storage space.
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.
