All Things Microsoft AI

In a recent episode of "Made for Dev" with Stephen Simon, I get into the world of AI Agents in Microsoft Foundry. I explain why agents are the "new microservices" and demonstrate how to build, configure, and orchestrate them using natural language.   What you’ll learn in this video: • What is an AI Agent? Understand the shift from LLMs that just answer questions to agents that execute business tasks [ 01:30 ]. • The "Physician" Analogy: Why an LLM is like a doctor who knows the theory but needs "tools" to actually take your blood pressure [ 02:05 ]. • Step-by-Step Demo: Watch Sam build a weather-forecasting agent from scratch in Azure AI Foundry, including tool integration via Logic Apps [ 04:54 ]. • Evaluation & Quality: How to interpret "Task Adherence" and "Intent Resolution" scores to ensure your agent is performing accurately [ 10:34 ]. • Advanced Orchestra...

One of the questions many tech leaders ask is “Which tool should I use for AI applications, Copilot Studio or Microsoft Foundry?”   Copilot Studio is a Low ‑ code, business ‑ friendly tool for the not-very technical user.  It’s fast to deploy and accessible at https://copilotstudio.microsoft.com   Microsoft Foundry (formerly known as Azure AI Foundry) is a Pro ‑ code tool designed to be more developer ‑ centric for the more technical user.  It offers full control, scalability, and is Azure native but accessed from a different URL ( https://ai.azure.com )   Summary    

One of the big topics I have been speaking about is “ Data Cleansing for Machine Learning ”.  A major part of the data cleansing process is filling in missing values.  These values could be missing for a variety of reasons (manually entered data, improper data collection, incorrect exception handling, etc.)  Missing data can be summed up into 3 categories: MAR: Missing at Random, for patterns tied to known variables (e.g., age, education) where someone did not want to answer a question. MCAR: Missing Completely at Random, for random glitches, accidental loss (i.e. sensor failure) MNAR: Missing Not at Random, describes a situation where data is missing because of the value of the missing data itself.   To counter the issue of missing data, several techniques are available to fill in missing data.  In my video , I discuss 3 of those techniques. Multiple Imputation by Chained Equations (MICE) handles numeric or...

User Groups Mar 3: Ohio North Database Training Mar 11: Azure Cleveland Mar 19: GLUG .NET Mar 25: Cleveland C# User Group   Conferences Feb – Jun: Agent Camp Mar 16: Memphis Agent Camp  

Yesterday I delivered a presentation on "Data Cleansing using Databricks".  Listed below are the questions that came up as well as the answers related to Databricks.   Does state transfer from one cell to another in the Databricks notebook? Yes, state does transfer from one cell to another, as long as you're running in the same notebook session and on the same cluster.   Can Databricks Jobs (aka "pipelines") be accessed through a notebook? Yes, you can fully interact with Jobs from a notebook using the Databricks REST API or the Databricks CLI. Some of these interactions from a notebook include: Trigger a job run Check job run status Cancel a run Retrieve job metadata   However, you can't directly "open" the Jobs UI from a notebook, and you can't modify job definitions without using the API   Listed below is an example for accessing a Job from a notebook import requests import json   token = dbutils.secre...

Retrieval ‑ Augmented Generation (RAG) has become one of the most important design patterns in modern AI because it gives language models direct access to external knowledge. Instead of relying solely on what a model has memorized during training, RAG systems retrieve relevant information from documents, databases, or other sources and feed it into the model at generation time. This idea dramatically improves accuracy, reduces hallucinations, and allows AI systems to stay current without constant retraining.   RAG has evolved into a rich ecosystem of architectural layers, addressing different challenges: Core Retrieval layer focuses on improving how information is found, from basic vector search to more advanced techniques like query expansion and hierarchical retrieval. Structure ‑ Aware layer organizes and interprets data based on relationships, formats, or time, enabling retrieval from graphs, tables, or multimodal sources. Reasoning ‑ Enhanced layer st...