Summary of Gen AI Roadmap | Generative AI Roadmap 2025

Summary of "Gen AI Roadmap | Generative AI Roadmap 2025"

This video presents a comprehensive, practical, and industry-informed 6-month roadmap to become a Generative AI (Gen AI) engineer, emphasizing strong fundamentals, consistent study, and real-world project experience. The speaker is the co-founder of ATL Technologies, with over 14 years of experience at Bloomberg and Nvidia, sharing a week-by-week study plan with free resources, assignments, and soft skills development.

Key Technological Concepts & Product Features Covered:

• Core Skills & Tool Skills:
  • Programming with Python (basics to advanced concepts like decorators, list comprehensions, multi-threading).
  • Data Structures and Algorithms fundamentals for scalable AI solutions.
  • Relational databases and SQL for data retrieval.
  • NoSQL databases (e.g., MongoDB) basics.
  • APIs and Backend Development using frameworks like FastAPI and Flask.
  • Docker for containerization and deployment.
  • Version Control Systems with Git and GitHub for collaborative coding.
  • Data manipulation and visualization using NumPy, Pandas, Matplotlib, and Seaborn.
  • Mathematics for AI: Linear algebra, calculus, statistics essential for model evaluation.
  • Statistical Machine Learning basics (logistic regression, naive Bayes), hybrid approaches with deep learning.
  • Deep Learning fundamentals with TensorFlow and PyTorch, including neural networks, optimizers, loss functions.
  • Natural Language Processing (NLP): tokenization, stemming, regular expressions, building chatbots (e.g., Dialogflow).
  • Generative AI Basics: vector databases, retrieval-augmented generation (RAG), LangChain, large language models (LLMs).
  • Advanced Gen AI Concepts: model fine-tuning, small language agents, agentic apps, multi-agent systems (MCP).
• Project-Based Learning:
  • End-to-end Python projects (e.g., grocery store app with UI, backend, database).
  • Plant disease classification project with cloud deployment (GCP/Azure).
  • Chatbot development with Dialogflow.
  • Kaggle notebooks for exploratory data analysis and machine learning.
  • Real client-based hybrid classification system project (statistical ML + LLM).
  • Hackathons and community project challenges for hands-on experience.
• Soft Skills & Career Development:
  • Building and maintaining a professional LinkedIn profile from day one, including meaningful engagement with AI influencers (e.g., Yann LeCun, Andrej Karpathy).
  • Writing technical blogs to increase visibility and credibility.
  • Public speaking and presentation skills via community events, hackathons, and Toastmasters.
  • Understanding project management methodologies like Scrum and Kanban; using tools like Jira and Notion.
  • Effective question-asking etiquette on Discord and other forums.
  • Open-source contribution to projects like Pandas to strengthen resume and interview chances.
  • Attending and volunteering at AI conferences for networking and mentorship opportunities.
  • Conducting mock interviews using ChatGPT for preparation.
  • Building a project portfolio website showcasing projects, GitHub repos, and personal introduction videos.
• Learning Strategy & Motivation:
  • Emphasis on consistent 4 hours/day study over 6 months with lifelong learning mindset.
  • Focus on fundamentals before jumping into advanced tools or shortcuts.
  • Use of AI tools like ChatGPT as a personal tutor and quiz master.
  • Avoiding distractions and balancing content consumption with active implementation.
  • Forming study groups via Discord for peer learning and motivation.
  • Real-life success stories of career transitions without formal CS degrees, highlighting discipline and structured learning.

Reviews, Guides, Tutorials, and Resources Provided:

• Curated YouTube playlists for Python, data structures, SQL, APIs, Docker, version control, deep learning (TensorFlow and PyTorch), NLP, and generative AI.
• Free roadmap PDF with links and checklists for each week.
• Use of platforms like Kaggle and Hugging Face for datasets, models, and notebooks.
• Recommendations for open-source projects to contribute to.
• Links to AI conferences and volunteering opportunities.
• Guidance on LinkedIn profile building and engagement.
• Tutorials on project management with Jira.
• Presentation and public speaking tutorials.
• Use of ChatGPT prompts for quizzes, coding help, and mock interviews.
• Bootcamp offerings by ATL Technologies for deeper learning.

Main Speaker / Source:

• Speaker: Co-founder of ATL Technologies, an AI services company.
• Background: 14+ years at Bloomberg and Nvidia.
• Role: Lead Gen AI engineer and mentor with real industry experience

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Technology

Video