AI System Design Introduction (From beginner to pro) скачать в хорошем качестве

AI System Design Introduction (From beginner to pro)

In this video, we will talk about AI System Design, we will break down how AI systems are built from data pipelines to model service. Whether you're new to AI or a software developer transitioning from traditional systems, this is your starting point. AI System Lifecycle - High-level Architecture 1. Data Sources - Where raw information originates — e.g., databases, APIs, user activity, sensors. 2. Data Pipelines - Processes that collect, clean, transform, and move data to the next stage. 3. Feature Engineering - Transforms raw data into meaningful inputs (features) used by the model. 4. Model Training - Uses historical data to teach the model how to make predictions. 5. Model Evaluation - Assesses the model’s accuracy, fairness, and generalization ability before deployment. 6. Monitoring / Drift Detection - Tracks model performance over time and detects shifts in data or predictions. 7. Model Registry - A version-controlled store of trained and validated models ready for deployment. 8. Model Serving - Exposes the model via APIs to serve predictions in real-time or batch. Types of AI 1. Predictive AI This is the most common and beginner-friendly AI — it helps you predict something, like whether a transaction might be fraud or what sales will be next month. These models often run as APIs or reports and are fairly easy to serve. 2. Classification AI Here, AI is acting like a decision-maker. Is this email spam? Is this image a dog or a cat? You give it labeled data, and it learns to sort things into categories. 3. Perceptual AI This type helps machines sense the world — they see, hear, or even feel. Think face ID or speech recognition. These models are larger and usually need GPUs to serve efficiently. 4. Generative AI This is the one everyone’s talking about — models that create things: text, images, code. These are your ChatGPTs. They often require a lot of compute, and some use external knowledge bases for better answers. 5. Reinforcement Learning These models learn from trial and error — like training a robot or a game agent to figure out the best moves. You don’t just feed it data, it learns from actions and feedback over time. 6. Recommendation Systems Ever wonder how Netflix picks shows for you? That's recommendation AI. It learns your preferences from past behavior and suggests things you might like, often combining several AI methods. TABLE OF CONTENTS • 00:00 Introduction • 00:35 What is Artificial Intelligence (AI)? • 02:23 Difference between Traditional Software System & AI System • 05:36 Analogy for software developers (code example) • 06:45 AI System Lifecycle - High-level Architecture of an AI System • 09:00 Types of AI and How they are Served FEATURED PLAYLISTS • System Design Patterns -    • FAANG System Design Patterns   • System Design Tradeoffs -    • FAANG System Design Tradeoffs   • Amazon Web Services (AWS) and Cloud Computing -    • Amazon Web Service (AWS)   • System Design Concepts -    • CAP Theorem & PACELC in Distributed System...   • Data Structures -    • FAANG Data Structures   • Object Oriented Design Concepts -    • FAANG Object Oriented Design Concepts   • Databases -    • Databases   Join this channel to get access to perks:    / @softwaredude