Software engineering is evolving fast. Hyper Velocity Engineering explores how LLMs, prompt engineering, and rapid prototyping are reshaping how we design, build, and reason about software. »
In the ever-evolving landscape of cloud-native applications, the need for scalable and resilient architectures has never been more critical. Traditional monolithic systems often fall short when it comes to handling large volumes of data and complex workflows. Enter Dapr: Distributed Application Runtime, a powerful framework that helps you build highly scalable and resilient microservices architecture. In this blog post, we'll explore how you can leverage Dapr to distribute your search ingest pip »
I often get the question: how do you manage your blog? Do you pay anything for hosting your blog? Do you have some sort of markdown interpreter? How about Search Engine Optimizations, can your blog posts be found online using Google? The short respective answers: Completely managed through a self-hosted Ghost instance, completely free, with rich editor and out of the box search engine optimizations. So, how does it work? Let's start with a list of requirements: * Running Ghost locally via Doc »
The Azure Search Index can be populated in two different ways. You can either directly push data into the index via the REST API/SDK (left image), or leverage the built-in Azure Search Indexer, which pulls data from a chosen DataSource and adds them to a specified index (right image) Choosing between the pull and push models depends on the requirements you have. Do you care most about how fast your data gets indexed, or is security your top priority? Maybe you need the flexibility to work with »
A great way to add insights to your unstructured PDFs is by ingesting them into an Azure AI Search Index and adding an LLM on top, also known as the RAG pattern as described in: Enhancing Your Data with Azure: Unlocking Insights through Retrieval-Augmented Generation. This blog posts shows various code snippets, on how to achieve these different steps using Python. The first bit is to setup and populate the Search Index, and the last sections show how to query the search index and enrich the da »
In the rapidly evolving world of data analytics and AI, new frameworks are popping up every day to enhance the way we interact with and understand our data. One of the most exciting developments is the integration of tailor-made Large Language Models (LLM) into business processes. These models, like Azure OpenAI's GPT-4, are transforming how companies get insights from their data. In this post, we'll dive into how you can leverage Retrieval-Augmented Generation (RAG) using Azure OpenAI and Azure »
It’s 2023, everyone is playing around with ChatGPT on OpenAI and a new and improved version of GPT is being released every couple of months with lots of new improvements which make you even more effective. The problem with these new models is that they’re only available for customers with OpenAI plus subscriptions, setting you back $20 each month (excluding tax). Instead of using OpenAI’s version, you can also setup your own gpt-4 instance on Azure OpenAI . Great! But what’s in it for me? * »
Databricks is great for processing data, and out of the box comes with 'Jobs' to automate and schedule notebooks. If you want more control over these triggers, customize the notebook parameters or have your own orchestrator, you could also trigger these notebook runs via another resource, such as a Web App. We can use the Web App's managed identity to run Databricks notebooks. This is a powerful way to automate and secure your data processing workflows, and it's surprisingly easy to set up. Wh »
Azure Kubernetes Service (AKS) comes with a Cluster Autoscaler (CA) that can automatically add nodes to the node pool based on the load of the cluster (based on CPU/memory usage). KEDA is active on the pod-level and uses Horizontal Pod Autoscaling (HPA) to dynamically add additional pods based on the configured scaler. CA and KEDA therefore go hand-in-hand when managing dynamic workloads on an AKS cluster since they scale on different dimensions, based on different rules, as shown below: Overv »
Setting up an Azure Kubernetes Service (AKS) using Terraform, is fairly easy. Setting up a full-fledged AKS cluster that can read images from Azure Container Registry (ACR), fetch secrets from Azure Key Vault using Pod Identity while all traffic is routed via an AKS managed Application Gateway is much harder. To save others from all the trouble I encountered while creating this one-click-deployment, I've published a GitHub repository that serves as a boilerplate for the scenario described above »