ISE Developer Blog
Solving global tech challenges, sharing insights, and empowering developers
Latest posts
Flexible Tool Selection for ML Model Production
data science and software engineering teams can choose the best tools for their respective roles in delivering machine learning models to production.
Multi Root Workspaces in Visual Studio Code
How to manage multiple independent python projects, with different dependencies, inside Visual Studio Code.
Multi-Provider Strategy for App Configuration in Python
This post discusses a strategy for creating a custom extensible configuration module in python projects.
Instrumenting Apache Spark Structured Streaming jobs using OpenTelemetry
Apache spark monitoring using OpenTelemetry
Unlock Generative AI for Enterprise Scalability
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
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
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
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
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.