AI Engineering Course
1h 36m 46s
English
Paid
AI Engineering Course is a 21-lesson 1 hour 36 minutes self-paced course by get.interviewready.io. 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
- Instructor
- get.interviewready.io
- 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? get.interviewready.io
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 |
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Read Book AI Engineering Course
| # | Title | Type | Open |
|---|---|---|---|
| 1 | 1. Vector+Embeddings+&+Semantic+Space | — | |
| 2 | 2. Compression+&+Quantization_+Scaling+Vectors+Efficiently-4 | — | |
| 3 | 3. Indexing+Techniques_+Making+Vector+Search+Scale | — | |
| 4 | 4. Search+Execution+Flow_+From+Query+to+Result | — | |
| 5 | 5. LLMs+and+RAG | — | |
| 6 | 6. What+is+Attention+and+Why+Does+It+Matter | — | |
| 7 | 7. Paged+Attention | — | |
| 8 | 8. Quantization+Summary | — |
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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.