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6-Month Roadmap — Expert in RAG & Enterprise LLM Solutions (Azure + on‑prem)

Goal: Design, build, secure, and operate production-grade RAG systems integrating enterprise data, using vector DBs, and applying adapters as needed.

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Month 0 — Foundations (1–2 weeks)

Outcome: Understand LLM fundamentals, embeddings, chunking, RAG architecture, and prompt basics.

• Coursera Module: Generative AI with LLMs — Modules 1–2 — https://www.coursera.org/learn/generative-ai-with-llms

svaza

🎯 Minimalist AI/ML Learning Plan for Principal Software Engineers & Architects

This curated list focuses on essential AI/ML knowledge to help you architect, lead, and guide AI/ML initiatives, without going deep into model-building.

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🧠 1. Understand AI at a Strategic Level

✅ AI for Everyone – Andrew Ng (Coursera)

• Platform: Coursera

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🎯 Azure Architect Expert Roadmap (Enhanced with Relevant Courses)

Objective

Become proficient in designing, implementing, and managing solutions on Microsoft Azure, focusing on both enterprise architecture and cloud infrastructure.

Prerequisites:

• Strong background in cloud computing concepts.
• Experience in software development, especially with .NET technologies.
• Knowledge of networking, security, and storage concepts.

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.NET Solution Architect - In-Depth Learning Roadmap

Phase 1: Core Technical Expertise

1. .NET Fundamentals & Advanced Concepts

• ✅ C# 12, LINQ, Async Programming, Memory Management
• ✅ .NET Core & .NET 8 (ASP.NET Core, API development, Dependency Injection)
• ✅ Clean Code, SOLID Principles, Design Patterns

📚 Courses:

svaza

ML Starter Roadmap for a .NET & Azure Developer

🎯 Goal:

To understand core ML concepts, build simple models, and integrate them with .NET & Azure.

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Phase 1: ML Fundamentals Without the Overhead (Month 1)

1️⃣ Basics of ML & AI

• Machine Learning Specialization by Andrew Ng (Course 1: Supervised ML)

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🎯 Generative AI Roadmap for Intelligent Application Development (With Azure Integration)

Objective

Leverage Generative AI and pre-trained ML models using APIs/SDKs (OpenAI, Azure, HuggingFace, etc.) to:

• Build intelligent business apps
• Solve real-world problems
• Integrate AI features into existing backends

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svaza

public class Solution {
  public int EdgeScore(int[] edges) {
    long[] deg = new long[edges.Length];
    long max = -1;
    for(int i = 0; i < edges.Length; i++)
    {
      deg[edges[i]]+=i;
      max = Math.Max(max, deg[edges[i]]);
    }
    

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public class Solution {
  public int ArithmeticTriplets(int[] nums, int diff) {
    var set = new HashSet<string>();
    
    int i = 0;
    int n = nums.Length-1;
    int c = 0;
   
    while(i <= n-2)
    {

svaza

public class Solution {
  public int LatestTimeCatchTheBus(int[] buses, int[] passengers, int capacity) {
    PriorityQueue<int, int> qbus = new();
    PriorityQueue<int, int> qpass = new();
    HashSet<int> set = new();
    
    foreach(var bus in buses) qbus.Enqueue(bus, bus);
    
    foreach(var p in passengers)
    {

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using System.Collections.Generic;

public class Solution {
  public IList<IList<int>> Permute(int[] nums) {
    
    IList<IList<int>> solution = new List<IList<int>>();
    Traverse(nums, new HashSet<int>(), new List<int>(), solution);
    return solution;
  }