AI Engineering Course
1h 36m 46s
English
Paid
Released
Added to CourseFlix
AI Engineering Course is a 21-lesson 1 hour 36 minutes self-paced course by Gaurav Sen (InterviewReady). This course is designed to help programmers and developers transition into the field of artificial intelligence engineering.
Course facts
- Lessons
- 21
- Duration
- 1 hour 36 minutes
- Language
- English
- Released
- June 2025
- Added to CourseFlix
- July 2025
- Instructor
- Gaurav Sen (InterviewReady)
- Price
- Premium
This course is designed to help programmers and developers transition into the field of artificial intelligence engineering. You will thoroughly explore vector databases, indexing, large language models (LLM), and the attention mechanism.
By the end of the course, you will understand how LLMs work and be able to use them to create real applications.
What you will learn:
- Develop mental models of how LLMs in the style of GPT work
- Understand processes such as tokenization, embeddings, attention, and masking
- Optimize LLM inference using caching, batching, and quantization
- Design and deploy RAG pipelines using vector databases
- Compare methods: prompt engineering, fine-tuning, and agent-based architectures
- Debug, monitor, and scale LLM systems in production
Who teaches AI Engineering Course? Gaurav Sen (InterviewReady)
get.interviewready.io is the paid course platform of Gaurav Sen, a software engineer (formerly at Uber) and one of the most widely watched system-design-interview educators on YouTube. His teaching style focuses on building the mental model from first principles — load balancers, sharding, queues, the trade-offs of CAP — rather than memorising specific architectures.
The CourseFlix listing carries his System Design Course and AI Engineering Course. Material is paid and aimed at engineers preparing for senior-level technical interviews at large tech companies, plus a newer track on building AI / LLM-powered systems.
What lessons are included in AI Engineering Course?
0:00
/ #1: Course Intro
All Course Lessons (21)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Course Intro Demo | 02:01 | |
| 2 | Usecase | 01:48 | |
| 3 | How are vectors constructed | 06:43 | |
| 4 | Choosing the right DB | 03:27 | |
| 5 | Vector compression | 03:27 | |
| 6 | Vector Search | 06:59 | |
| 7 | Milvus DB | 05:38 | |
| 8 | LLM Intro | 00:43 | |
| 9 | How LLMs work | 08:31 | |
| 10 | LLM text generation | 03:08 | |
| 11 | LLM improvements | 05:10 | |
| 12 | Attention | 05:28 | |
| 13 | Transformer Architecture | 03:40 | |
| 14 | KV Cache | 08:28 | |
| 15 | Paged Attention | 04:38 | |
| 16 | Mixture Of Experts | 04:01 | |
| 17 | Flash Attention | 03:40 | |
| 18 | Quantization | 03:33 | |
| 19 | Sparse Attention | 05:14 | |
| 20 | SLM and Distillation | 05:31 | |
| 21 | Speculative Decoding | 04:58 |
Watch the other 20 lessons
One payment unlocks this course and every other premium course. No auto-renew.
See plansBooks
Read Book AI Engineering Course
| # | Title | Type | Open |
|---|---|---|---|
| 1 | 1. Vector+Embeddings+&+Semantic+Space | — | Unlock |
| 2 | 2. Compression+&+Quantization_+Scaling+Vectors+Efficiently-4 | — | Unlock |
| 3 | 3. Indexing+Techniques_+Making+Vector+Search+Scale | — | Unlock |
| 4 | 4. Search+Execution+Flow_+From+Query+to+Result | — | Unlock |
| 5 | 5. LLMs+and+RAG | — | Unlock |
| 6 | 6. What+is+Attention+and+Why+Does+It+Matter | — | Unlock |
| 7 | 7. Paged+Attention | — | Unlock |
| 8 | 8. Quantization+Summary | — | Unlock |
What courses are similar to AI Engineering Course?
Updated 1mo agoLocal AI Revolution: Ollama and OpenClaw
By: Zero To MasteryPractical course on creating a local AI platform with Ollama and OpenClaw. Learn to deploy LLM, configure GPU, and build autonomous agents.2h 51m
ClassicThe AI Engineering Bootcamp
By: Dr. Greg Loughnane, Chris AlexiukThe AI Engineering Bootcamp is an intensive 10-week program designed to equip participants with the skills necessary to excel as an AI Engineer.22h 13m5/5
Updated 5mo agoGOLANG A.I. Masterclass - Build 6 A.I. Projects with GO (Advanced Course)
By: Akhil SharmaTake a 26-hour course on AI project development in Golang. Create 6 advanced projects, enhancing your skills in building scalable solutions.25h 50m0/5
Updated 2y agoBuild an AI Career Coach using an Open Source LLM
By: Zero To MasteryCreate your own AI-based career coach using an open LLM and prompt management techniques!1h 38m5/5
Updated 3mo agoDesigning AI Systems
By: Suhas Suresha, Dewang SultaniaThe book reveals the architecture of AI and the creation of AI platforms, helping design scalable and secure systems for work applications.
Updated 4mo agoCloud AI Integrations
By: Zen van RielMaster the practical implementation of AI models in applications using cloud services. Learn to work with APIs, scalable AI services, and create prototypes.27m
NewDesigning Production Ready RAG System Crash Course
By: Youssef HosniLearn to design a Production RAG: PDF processing and OCR, chunking, hybrid search, metadata, access rights, and integration with an AI agent.1h 42m
ClassicBuild AI-Powered Apps – An AI Course for Developers
By: Mosh Hamedani (Code with Mosh)AI is everywhere - but can you really create applications with it? Most developers have tried ChatGPT.7h 3m5/5
More courses by Gaurav Sen (InterviewReady)
ClassicSystem Design Course
In this course, you’ll get a deep understanding of system design concepts. Full guide. Architectural schemes, capacity planning, API list, tests, Faq.92h 26m4/5
Frequently asked questions
What prerequisites are needed for this AI Engineering course?
Prospective students should have a solid understanding of programming and software development. Familiarity with concepts in databases and machine learning would be beneficial, as the course covers topics like vector databases, indexing, and large language models (LLM).
What kind of projects or exercises will I work on in this course?
The course includes practical exercises in vector search and the use of vector databases like Milvus DB. Students will explore how vectors are constructed and compressed, and how these concepts apply to large language models and the attention mechanism.
Who is the target audience for this AI Engineering course?
This course is aimed at programmers and developers who are looking to transition into the field of artificial intelligence engineering. It is suitable for individuals interested in learning about vector databases, large language models, and advanced topics like the attention mechanism and sparse attention.
How does this course compare in depth to other AI courses?
This course offers a focused exploration of specific AI engineering concepts such as vector databases, LLMs, and attention mechanisms, including topics like transformer architecture, KV cache, and speculative decoding. It provides detailed coverage of these areas, which may not be as deeply explored in more general AI courses.
What specific tools or platforms will I learn about in this course?
Students will learn about Milvus DB, a platform for vector databases, as well as concepts related to large language models and attention mechanisms. The course covers various types of attention including flash attention, sparse attention, and paged attention.
What topics are not covered in this AI Engineering course?
The course does not cover introductory programming or general machine learning techniques. It focuses specifically on vector databases, large language models, and attention mechanisms, without delving into broader AI topics such as neural networks or supervised learning techniques.
How can the knowledge from this course be applied to other courses or careers?
The skills gained from this course in vector databases, LLMs, and attention mechanisms are applicable to careers in AI research and development. Understanding these advanced topics can also be beneficial for further studies in AI, particularly in fields related to natural language processing and data indexing technologies.