Description
AI System Design for Engineers is a course on designing, architecting, and deploying production-ready AI applications at scale, published by Udemy Online Academy. With the help of this course, students will learn how to build end-to-end AI systems by combining machine learning models, large language models (LLMs), data pipelines, vector databases, and cloud infrastructure. The course covers key topics such as AI architectural patterns, retrieval-based additive manufacturing (RAG), model services, rapid engineering, scalability, latency optimization, monitoring, security, and cost management. Students will also explore system design tradeoffs, distributed architectures, evaluation strategies, and best practices for integrating AI capabilities into real-world products.
As AI adoption increases, companies need engineers who understand not only the models and APIs, but also the architecture, scalability, reliability, and infrastructure behind production AI systems. Through hands-on case studies, you’ll learn how technologies like LLMs, RAG, AI Agents, Kafka, Redis, Kubernetes, Vector Databases, FastAPI, and Databricks work together to power enterprise AI applications. You’ll also learn about architectural patterns used in machine learning, supervised learning, unsupervised learning, semi-supervised learning, natural language processing, and computer vision systems. This course will help you think like a senior AI engineer and AI architect.
What you will learn in AI System Design for Engineers:
- Design production-ready AI systems by translating business requirements into scalable architectures.
- Architect modern AI applications using LLMs, RAG, AI agents, vector databases, Kafka, Redis, and Kubernetes.
- Analyze real-world engineering tradeoffs including scalability, reliability, latency, throughput, and cost optimization.
- Design machine learning, natural language processing, and computer vision systems used in production environments.
- Evaluate and select appropriate architectural patterns for AI platforms, inference systems, data pipelines, and distributed services.
- Decompose complex AI products into scalable system components and confidently communicate architectural decisions.
- Understand how leading technology companies design and scale AI systems that serve thousands to millions of users.
- Answer AI system design and machine learning system design interview questions using a structured engineering framework.
- And …
Course specifications
Publisher: Udemy
Instructors: Aritra Basak
Language: English
Level: Introductory to Advanced
Number of Lessons: 36
Duration: 7 hours and 5 minutes
Course topics
AI System Design for Engineers Prerequisites
Familiarity with software engineering fundamentals such as coding, APIs, databases, caching, and application deployment will help you get the most value from this course.
A basic understanding of Artificial Intelligence and Machine Learning concepts, including LLMs, NLP, Computer Vision, or modern AI applications, is recommended.
This course focuses on architecture and system design discussions rather than implementation, so learners should be comfortable understanding technical workflows, engineering trade-offs, and production system concepts.
Pictures
AI System Design for Engineers introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
6.7 GB
