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ISE Developer Blog
Solving global tech challenges, sharing insights, and empowering developers
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![Unlock Generative AI for Enterprise Scalability](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2025/02/main_image.png)
Unlock Generative AI for Enterprise Scalability
![Shiran Rubin Manaev](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2022/06/Untitled-2-96x96.jpg)
Reusable templates, pipelines, and tools to streamline the setup of GenAI projects, saving time and effort, and ensuring consistency and reliability across projects.
![Azure Functions vs. Indexers: AI Data Ingestion](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2025/02/Unlock-AI-Search-Potential-The-Case-for-Azure-Functions-in-Data-Ingestion-DALL-e.jpg)
Azure Functions vs. Indexers: AI Data Ingestion
This article compares Azure Functions with pre-built indexers for data ingestion in Azure AI Search, with a focus on using Azure Functions for a flexible, scalable approach. It explores key steps like data migration, index creation, and deployment automation.
![Using Managed Identity on Logic App consumption](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2025/01/logic_app_managed_identity.jpeg)
Using Managed Identity on Logic App consumption
Deploy Azure Logic App Consumption with Managed Identity using Terraform by leveraging azapi_resource and ARM templates. This setup enables secure authentication without manual credential management.
![Revolutionising Customer Feedback: Harnessing Large Language Models for Retail Insights and Intelligence](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2025/01/insights-generation-icon-1.png)
Revolutionising Customer Feedback: Harnessing Large Language Models for Retail Insights and Intelligence
![Shinoj Zacharias](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2023/06/shinojMS-96x96.jpg)
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.
![LLMOps in restricted networks and addressing continuous evaluation long run constraints](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2025/01/overall-flow-pipeline.png)
LLMOps in restricted networks and addressing continuous evaluation long run constraints
In this blog post, we'll explore the challenges we faced in establishing LLMOps and continuous evaluation within a pipeline using Azure Machine Learning, particularly when dealing with long runs and a restricted Bring-Your-Own (BYO) network.
![Transforming Language into Code: Building and Evaluating a Robotic Code Generation Copilot](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2024/12/code_generation_image.jpeg)
Transforming Language into Code: Building and Evaluating a Robotic Code Generation Copilot
This blog post explores the development and evaluation of an AI assistant that converts natural language into robotic code.
![Implementing Evaluation platform on Azure Machine Learning](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2024/12/implement-evaluation-platform-on-aml.jpg)
Implementing Evaluation platform on Azure Machine Learning
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.
![Semantic Kernel Learnings](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2024/12/ai-powered-kernel-thumbnail.png)
Semantic Kernel Learnings
A synopsis of our key learnings and findings from using Semantic Kernel.
![Building AI Search for Production in Corporate Environments: Key Lessons](https://devblogs.microsoft.com/ise/wp-content/uploads/sites/55/2024/11/featured_image.png)
Building AI Search for Production in Corporate Environments: Key Lessons
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 generating syntactic datasets, embedding models like text_embedding_3, and Azure AI Search for implementing both text and hybrid search solutions. Here are 10 key lessons we learned along the way.