ISE Developer Blog
Solving global tech challenges, sharing insights, and empowering developers
Latest posts
Analysing Bot to Human Hand-off Interactions
A common use case in the bot-verse is the ability to hand over a customer conversation from a bot to a human, and the vital need for a feedback loop for this kind of scenario. From this, we developed a Hand-off Dashboard Template that can be used within the Ibex dashboard to provide users with important information about how their bot and agents are working with customers.
Orchestrating TURN Servers for Cloud Deployment
We worked with Aveva to build 3DToolkit, a toolkit for creating powerful cloud-based 3D experiences that stream on low-powered devices with WebRTC.
Azure Event Hub Ingestion at Scale with Python and Kubernetes
We created a solution to ingest Azure Event Hubs events at scale using Python and Kubernetes.
Continuous Delivery for Service Fabric via Github, Travis CI and Docker Compose
This article describes how to use GitHub, Travis CI and Docker Compose to build a simple continuous delivery pipeline to deploy Linux Docker containers to a Service Fabric cluster of Linux hosts.
Deploying a Linux Python web application to Service Fabric via Docker Compose
This article covers how to take a standard Python web service consisting of an application tier, a WSGI server, and a Nginx reverse proxy and deploy it via Linux containers to a Linux cluster managed by Azure Service Fabric using only simple tooling like Docker Compose.
Giving meaningful context to social media influence with Microsoft Cognitive Services
When it comes to identifying and understanding influential voices in social media, context is key. See how we used topic detection, sentiment analysis, and Azure Functions to automate context-aware social media insights.
Comparing Image-Classification Systems: Custom Vision Service vs. Inception
This story covers how to get started with transfer-learning and build image classification models in Python with the Custom Vision Service. We compare the results with the popular Tensorflow-based models Inception and MobileNet.
Stock Market Predictions with Natural Language Deep Learning
We developed a deep learning model using a one-dimensional convolutional neural network to predict future stock market performance of companies using Azure Machine Learning Workbench and Keras.
Autoscaling Deep Learning Training with Kubernetes
We explore how we worked with a customer to add autoscaling capability to a Kubernetes cluster to meet bursty demands for deep learning training in a cost-efficient manner.