CF

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
Level
All levels
Language
English
Updated
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 thumbnail

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?

This is a demo lesson (10:00 remaining)

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#1: Course Intro
All Course Lessons (21)
#Lesson TitleDurationAccess
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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Books

Read Book AI Engineering Course

#TitleTypeOpen
11. Vector+Embeddings+&+Semantic+Space PDF
22. Compression+&+Quantization_+Scaling+Vectors+Efficiently-4 PDF
33. Indexing+Techniques_+Making+Vector+Search+Scale PDF
44. Search+Execution+Flow_+From+Query+to+Result PDF
55. LLMs+and+RAG PDF
66. What+is+Attention+and+Why+Does+It+Matter PDF
77. Paged+Attention PDF
88. Quantization+Summary PDF

What courses are similar to AI Engineering Course?

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.