Complete AI Engineer Roadmap: Backend Dev's Journey from Python to LLMs

[Music] Hey everyone, welcome back to my channel. As you all know, I've been a backend engineer for the past several years working with technologies like Python, AWS, Kubernetes, and building data pipelines. But lately, I've been fascinated by the incredible developments in AI and machine learning. Over the past few weeks, I've been spending a lot of time wrapping my head around what it would take to really understand AI deeply. Not just to use it, but actually build AI systems from the ground up. And you know what? Some topics absolutely excited me, while others, well, let's just say they didn't spark the same enthusiasm initially. But I realized something. I needed to start somewhere. And I felt the best way to learn is to learn in public and document the entire journey. So that's exactly what this and the upcoming videos are about. I'm going to be documenting my complete learning journey as I dive deep into artificial intelligence and machine learning. Every concept I learn, every project I build, every mistake I make, I'll be sharing it all with you. This isn't about me being an expert teaching you. This is about us learning together. And hopefully my documentation of this process can help others who are on the similar path. Now before I jump into what I'll be covering, let me tell you about the research I did to create this road map. I spent considerable time analyzing current AI engineering job postings over thousands of them from companies ranging from startups to major tech giants like Google, OpenAI and Microsoft. What I discovered was fascinating. Python dominates 71% of job requirements. This is a great news for me since I already know this. Natural language processing is the most in demand AI skill. Cloud platforms like AWS and Azure are crucial. Companies are heavily preferring specialists over generalists. And coming to the salaries of a engineers, the average a engineer salary has jumped to over $200. But here's the thing, I didn't want to just chase what's hot right now. I wanted to build a foundation that would serve me well for the next 3 to 5 years regardless of how the field evolves. My approach is going to be different from typical AA courses you might see online. Instead of just covering theory, I'm going to focus heavily on understanding the fundamentals deeply so when new techniques come out, I can adapt quickly. Building real projects because that's what actually matters in the industry. Production focus leveraging my backend experience to focus on getting AI into real systems. And as I said earlier, learning in public will be our moto. All right, let me walk you through the complete root map I have designed. I will be organizing everything into playlists that represent major learning areas. Now, I want to be upfront. I probably won't follow this order exactly when recording videos. If something particularly interests me or if I get stuck on a concept, I might jump around. The beauty of playlist is that you can watch them in whatever order makes sense for your learning style. Mathematical foundations. This is where it all starts. You can't build AAI systems without understanding the math underneath. Topics that I could think of as of now are linear algebra, statistics and probability, calculus, and information theory. Next comes ML fundamentals. The core concepts that everything else builds upon supervised and unsupervised learning, model evaluation and validation, feature engineering, bias variance trade-off, cross validation strategies. Modern AI is built on deep learning. So this is crucial. Under this we'll be covering neural networks back propagation, CNN's, RNN's, PyTorch and TensorFlow along with advanced optimization techniques. Another critical pieces natural language processing. Since NLP is the most in demand skill, this gets major focus. Under this, I'll be covering text processing and tokenizations, word embeddings and language models. We will also deep dive into transformer architecture, attention mechanisms, bird and je family models along with hugging face ecosystem. We will be having one complete playlist dedicated to natural language processing. And another playlist includes cutting edge stuff that's driving the current AI revolution and that is large language models and modern AI. Under this we'll be discussing LLM architecture and training prompt engineering racks vector databases and embeddings lang chain framework AI agents and multi- aent systems. In addition to all this, we'll have a dedicated playlist for computer vision. Visual AI is huge and I want to understand how machines see. So to understand it better, we'll be discussing image processing fundamentals, CNN's, convolution neural networks for vision, object detection, image segmentation, generative adversarial networks which are GANs. We'll be also discussing stable diffusion and image generation and then comes the most in demand skill MLOps. This is where my backend experience really shines getting AI into production. In this playlist, we'll be covering containerizing ML models, Kubernetes for ML workloads, model serving with fast API, experiment tracking with ML flow, model monitoring and maintenance, CACD pipelines for ML, AB testing for AI systems. Next, we'll also be stepping into the most essential component for modern AI development. Under cloud AI platforms, I'll be covering different AI aspects in AWS, Azure and Google cloud along with taking a peek into serverless ML with AWS Lambda. Also, we'll be having a lot of discussions on building scalable AI infrastructure. All the topics that we have discussed till now is the most happening tech around us today. Apart from all this, I'll be discussing multiple advanced AI techniques that are listed here along with some AI ethics and safety. One important thing to note while I have laid out this comprehensive road map, I'm not going to be rigid about following it in order. If I'm working through linear algebra and suddenly get excited about building a chatbot, I might jump ahead and create that content first. The playlists are designed so you can consume the content in whatever order makes sense for your own learning journey. Maybe you already know the math and want to jump straight to deep learning. Maybe you are more interested in practical MLOps stuff. That's totally fine. I might also add new playlists or topics as I discover areas that particularly interest me or that seem crucial based on what I'm learning. So yeah, time to wrap up this video. If this kind of content sounds interesting to you, whether you are also looking to learn AI or you're already in this field and want to see a different perspective or you just enjoy following someone's learning journey, please consider subscribing and hitting that notification bell. Also, if you have suggestions for topics you would like me to cover or if you're on the similar learning path and want to share your experiences, drop a comment below. Thanks for watching and I'll see you in the next video. Until then, stay tuned, stay focused, keep learning.