AI Engineering for .NET Developers

About Course

Become an AI Engineer with Your Existing .NET Skills

AI Engineering for .NET Developers is a comprehensive 180-day, project-based course designed for C#, ASP.NET Core, SQL Server and Azure developers who want to move into professional AI engineering without abandoning their existing technology stack.

The course begins from absolute AI foundations and progresses step by step through data preparation, mathematics, statistics, machine learning, deep learning, natural language processing, generative AI, large language models, prompt engineering, embeddings, vector databases, Retrieval-Augmented Generation (RAG), AI agents, evaluation, security, deployment and LLMOps.

Every lesson is designed as a focused one-hour study session. New concepts are first explained in simple language and connected with familiar C#, SQL and software-engineering concepts. Each session then includes a clear text diagram, practical example, hands-on exercise, recap quiz and structured notes.

What You Will Learn

Understand AI, machine learning, deep learning and generative AI from the ground up.
Recognize which business problems require deterministic rules, machine learning, generative AI or a hybrid solution.
Prepare, clean, transform and validate structured and unstructured data for AI systems.
Understand the mathematics and statistics required for practical machine learning.
Build and evaluate classification, regression, forecasting, clustering and anomaly-detection models.
Use ML.NET to train models and integrate predictions into .NET applications.
Understand neural networks, deep learning, computer vision and natural language processing.
Understand large language models, tokens, context windows, inference and model limitations.
Design effective prompts and structured AI outputs for production applications.
Connect ASP.NET Core applications with AI models and services through secure APIs.
Create embeddings and implement semantic search using vector databases.
Build production-ready RAG applications that answer questions from trusted documents and databases.
Develop tool-using AI agents and orchestrate multi-step workflows.
Implement human approval, safety controls, permissions and audit trails in agentic systems.
Evaluate AI responses for accuracy, relevance, groundedness, safety, latency and cost.
Deploy and monitor AI applications using .NET, SQL and Azure.
Apply LLMOps practices including prompt versioning, model configuration, observability, testing and continuous improvement.
Build portfolio-grade AI projects that solve realistic business problems.

Course Roadmap

AI and Data Foundations — Understand AI terminology, data types, datasets, features, labels, data quality and the AI development lifecycle.
Mathematics and Statistics Essentials — Learn only the practical mathematics required to understand and evaluate machine-learning models.
Machine Learning — Build supervised and unsupervised learning solutions using practical business datasets.
Deep Learning and NLP — Understand neural networks and create applications that work with text, language, images and complex patterns.
LLMs and Prompt Engineering — Learn how language models work and how to obtain reliable, structured and controlled outputs.
Embeddings, Vector Databases and RAG — Build AI assistants that retrieve and answer from trusted organizational knowledge.
AI Agents — Create agents capable of selecting tools, calling APIs and completing controlled multi-step tasks.
Evaluation, Security and LLMOps — Test, secure, deploy, observe and improve production AI applications.
Professional Capstone Projects — Combine ASP.NET Core, C#, SQL, Azure and AI into deployable portfolio projects.

Practical Projects

Throughout the course, learners will build progressively more capable projects, including:

Rule-based versus machine-learning support-ticket router
Customer churn or policy-renewal prediction system
Fraud or unusual-activity detection prototype
Text classification and sentiment-analysis application
ASP.NET Core generative-AI assistant
Semantic document search application
RAG-based organizational knowledge assistant with citations
Tool-using AI agent with human approval controls
Production AI application with evaluation, logging and monitoring
Final enterprise AI Engineering capstone project

Technology Stack

C# and modern .NET
ASP.NET Core and REST APIs
SQL Server and relational data
ML.NET
Semantic Kernel
Azure AI services and cloud deployment
Embeddings and vector databases
Large language model APIs
Git and practical deployment workflows
Python only when a specific AI or data-science workflow genuinely requires it

Who This Course Is For

.NET and C# developers moving into AI Engineering
ASP.NET Core developers who want to add AI capabilities to business applications
SQL developers who want to use organizational data for prediction and intelligent search
Software architects and technical leads planning production AI systems
Working developers who want a structured path from AI fundamentals to LLMOps

Prerequisites

Learners should understand basic C# programming, classes, methods, collections, SQL queries and application development. Previous knowledge of artificial intelligence, machine learning, advanced mathematics or Python is not required.

Learning Format

The course contains 180 structured one-hour sessions. Each daily lesson includes:

5-minute orientation and learning objectives
15-minute beginner-friendly concept explanation
5-minute visual or text-based concept diagram
15-minute practical .NET, C# or SQL example
10-minute hands-on exercise
5-minute recap quiz
5-minute note-making and progress checkpoint

Python Policy

This is not a Python-first course. C#, .NET, SQL and Azure remain the primary technologies. Python is introduced only when an important machine-learning library, notebook workflow, model-training process or AI ecosystem tool makes it genuinely useful. Whenever Python is introduced, its syntax and concepts are mapped to equivalent C# concepts.

Course Outcome

By the end of the course, learners will be able to design, build, evaluate, secure, deploy and monitor production-oriented AI applications using their existing .NET development experience. They will also complete practical projects suitable for demonstrating AI Engineering skills in a professional portfolio.

Course Content

What AI actually is: AI vs ML vs Deep Learning vs Generative AI
Learning objectives By the end of this session, you should be able to: Explain AI in simple language. Distinguish rule-based software, ML, deep learning, and generative AI. Identify whether a business problem genuinely needs AI. Understand the basic flow: data → training → model → prediction. Relate AI development to familiar C# and SQL concepts.

Data Foundations: Datasets, Features, Labels and Data Quality
After completing this, you will be able to: Explain what a dataset represents in machine learning. Distinguish features, labels and identifiers. Recognize structured, semi-structured and unstructured data. Identify missing, duplicated, inconsistent and potentially leaked data. Design a basic ML-ready SQL dataset. Validate training data using SQL and C#.

The AI/ML Project Lifecycle: From Business Problem to Measurable Solution
Learning objectives Decide whether a requirement genuinely needs AI. Convert a vague business request into a measurable ML problem. Define the prediction unit, label, prediction time and business action. Distinguish business KPIs from model metrics. Establish a simple baseline before training a model. Identify false-positive and false-negative business costs. Document an AI use case using a reusable project canvas.

Training, Validation and Test Datasets
Learning objectives Explain the purpose of training, validation and test datasets. Understand overfitting and generalization. Create reproducible data splits with ML.NET. Choose between random, grouped and time-based splitting. Prevent duplicate-entity and preprocessing leakage. Protect the final test set from repeated use.

Supervised, Unsupervised and Reinforcement Learning
Learning objectives After this lesson, you will be able to: Distinguish supervised, unsupervised and reinforcement learning. Choose the appropriate learning approach for a business problem. Identify classification, regression and clustering tasks. Understand the roles of labels, clusters, agents, actions and rewards. Avoid treating every AI requirement as a classification problem. Map real .NET projects to appropriate ML tasks.

Feature Engineering Fundamentals
Learning objectives After this lesson, you will be able to: Explain what feature engineering means. Separate SQL eligibility rules from AI predictions. Convert categories, dates and transactional history into model-friendly features. Understand encoding, scaling, aggregation and feature vectors. Maintain point-in-time correctness and prevent feature leakage. Build a basic ML.NET feature-engineering pipeline.

Data Pipelines and Reproducibility
Learning objectives After this lesson, you will be able to: Explain an ML data pipeline from SQL extraction to production prediction. Distinguish training pipelines from scoring pipelines. Understand reproducibility, data snapshots, lineage and schema contracts. Prevent training-serving skew. Record the data, code, parameters and model used in each experiment. Design a repeatable C#/SQL/ML.NET workflow.