AI innovation teams
A practical look at standing up AI innovation teams that deliver measurable outcomes—not just pilots.
- Building AI Innovation Teams That Actually Deliver — frameworks for structuring roles, workflows, and accountability.
- Bridging the Gap: How to Integrate AI Innovation Teams into Existing Business Operations — Integrating AI Innovation Teams with Existing IT and Business Units.
- From Experiments to Impact: How to Measure the ROI of AI Innovation Teams — Measuring the ROI and Performance of AI Innovation Teams.
- For a broader overview of this topic, see our in-depth resource: How .NET Teams Can Get Started with AI.
AI Devops
How to weave AI workloads into mature .NET DevOps practices while preserving reliability and speed.
- AI DevOps in the .NET Environment — patterns for build, test, deploy, and monitor across AI services.
- How to Integrate AI into Your CI/CD Pipelines: The Next Step in .NET DevOps Evolution — Integrating AI into CI/CD Pipelines for Smarter DevOps in .NET.
- Beyond Automation: How Predictive AI Monitoring Transforms .NET DevOps Pipelines — Predictive AI Monitoring for Continuous Improvement in .NET DevOps.
- For a broader overview of this topic, see our in-depth resource: AI Compliance and Security: How to Build Trust in Enterprise AI.
Microsoft AI
A deep dive into Microsoft’s Semantic Kernel and how it orchestrates prompts, tools, and memory for production-grade assistants.
- Ultimate Guide on What is Semantic Kernel in Microsoft AI? — concepts, components, and real integration patterns.
- How to Build Custom Copilot Solutions in .NET Using Microsoft’s Semantic Kernel — Building Custom Copilot Solutions with Semantic Kernel in .NET.
- How Semantic Kernel Orchestrates AI Workflows: The Hidden Power Behind Microsoft’s AI Framework — Orchestrating AI Workflows with Semantic Kernel: How Microsoft’s framework unifies logic, memory, and prompts.
- For a broader overview of this topic, see our in-depth resource: AI Tools for .NET Developers: Choosing the Right Stack with Confidence.
Training and Deploying models in ML.NET
Resources for taking ML.NET models from prototype to production—then keeping them fast as data evolves.
- Training and Deploying Models in ML.NET: A Walkthrough — end-to-end training, evaluation, and deployment steps.
- How to Optimize ML.NET Model Performance Using Real-World Data Pipelines — Optimizing ML.NET Model Performance with Real-World Data Pipelines.
- How to Automate ML.NET Model Retraining and Deployment Using CI/CD Pipelines — Automating ML.NET Model Retraining and Deployment with CI/CD Pipelines.
- For a broader overview of this topic, see our in-depth resource: Scaling AI with Microsoft Tools.