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.
In the dynamic world of AI and data science developing production-level solutions for corporate environments comes with its own set of challenges and lessons. As a data science team working within Microsoft, we recently completed an engagement for a large company where we leveraged cutting-edge technologies, including OpenAI tools, GPT-4o for gener...
This blog shares insights on developing a GenAI gateway with multi-tenancy and quota management capabilities implemented using Azure APIM where customers can access the GenAI resources across different service tiers like Freemium, Basic, and Premium with each tier having it's own quota and rate limits. The solution used the concept of "Products" to...
This blog post delves into the experimentation journey of fine-tuning a multimodal RAG pipeline to best answer user queries that require both textual and image context. We ran our experiments by systematically testing various approaches, adjusting one configuration setting at a time and using clearly defined evaluation metrics to validate the perfo...
This post explores effective error handling strategies in Power Automate to enhance workflow reliability and maintainability through practical techniques and integrations.
In this post we discuss how to test the throughput of PromptFlow pf-serve module and key learnings doing so. We explore the impact on throughput and performance the different WSGI and ASGI hosting methods have and the importance of engineering your Python nodes with the async await pattern for I/O.