Sometimes you must step back,not to retreat,but to see. From a little distance,the picture sharpens,edges find their meaning,noise falls silent. And in that pause,in the space you create,you find the roomto leap forwardwith purpose.
First there were hunters and gatherers,men who knew the patience of forests,who read the language of windand measured time by the sun’s slow breathing. They took only what hunger asked for,and the earth, like a quiet mother,kept no account of their debts. Then came the poets;and all hell broke loose. For poets do not merely... Continue Reading →
Forever feels right with you; like the first soft light that breaks the night through,your presence steadies the storms I never knew.Your touch: gentle as prayer, fills me with joy,and every heartbeat whispers, I belong to you. Forever feels right with you;like monsoon rains finding the earth they once knew.Your smile is a doorway my... Continue Reading →
Automating your Power BI dataset refresh can save time and ensure your reports always stay up to date. In this post, we’ll walk through how to trigger a Power BI report refresh directly from Azure Data Factory (ADF) using an App Registration in Azure Entra ID.
There was a time when moving data from multiple sources felt like untangling a giant knot. Every data refresh meant scripts breaking, manual checks, and long hours spent ensuring everything flowed from source to destination correctly. Then Azure Data Factory (ADF) entered the picture, and it didn’t just simplify ETL and ELT. It completely transformed how we think about data orchestration at scale.
Online spaces thrive on trust. Whether it’s a social network, a community forum, or a brand encouraging customers to share their experiences, user-generated content is at the heart of digital interaction. People tend to trust authentic voices more than marketing copy, and businesses actively promote this trust as part of their strategy.
Did you know you can run Azure AI services on your own infrastructure using containers? Microsoft provides container images for individual AI service APIs through the Microsoft Container Registry (MCR). This allows you to deploy AI closer to your data on-premises, in Azure, or even at the edge.
As engineers, we don’t just build systems. We shape experiences that affect people and society. AI is powered by probabilistic models trained on data, which means it can sometimes amplify biases or make mistakes that impact real lives. This is why principles of Responsible AI matter. At Microsoft, several core principles guide the responsible development and deployment of AI systems. Let’s look at them one by one.
Azure AI Foundry is a powerful platform for developing and scaling AI solutions. It gives teams structure through hubs and projects, shared resources, and collaborative tools. But to get the most from Foundry, it is important to plan carefully. From resource organization to cost management, a little forethought can make your AI journey smoother and more efficient.
A project in Azure AI Foundry is a workspace designed for a specific AI development effort. Each project connects to a hub, giving it access to shared resources while also providing its own dedicated environment for collaboration and experimentation.
A hub is the foundation of Azure AI Foundry. Think of it as a control center where all the shared resources, security settings, and configurations for your AI development live. Without at least one hub, you cannot use the full power of Foundry’s solution development capabilities.
Azure AI Foundry is Microsoft’s dedicated platform for building, managing, and scaling AI solutions in the cloud. It is not just a collection of services, it is a structured environment designed to make AI development more efficient, organized, and secure.
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