From Software Engineer to AI Engineer: This Learning Path Will Guide Your Breakthrough Transition!

Overview of the Project

InterviewReady/ai-engineering-resources is an open-source project created by Gaurav Sen, a former Google engineer and the founder of InterviewReady. It is designed to help software engineers transition into AI engineering through a curated collection of high-quality resources. The repository aggregates cutting-edge research papers, blog articles, and tools that span foundational concepts to practical applications in AI development.

Main Modules & Topics

The repository is well-organized into several core areas, each covering a critical aspect of AI engineering:

1. Tokenization

Byte-Pair Encoding (BPE): A widely-used subword tokenization technique that helps handle out-of-vocabulary words.
Byte Latent Transformer: A model that processes byte-level inputs for better multilingual and multimodal performance.

2. Vectorization

BERT: A deep bidirectional Transformer model for language understanding.
IMAGEBIND: Projects multiple modalities (text, image, audio) into a shared embedding space for multimodal learning.
SONAR: Provides language-agnostic, sentence-level multimodal representations.
FAISS: A fast similarity search library developed by Facebook for efficient vector retrieval.

3. Infrastructure

TensorFlow: A widely-used deep learning framework for building and training AI models.
DeepSeek File System: Optimized for large-scale training workloads and efficient data access.
Milvus: An open-source vector database supporting scalable vector data storage and retrieval.
Ray: A framework for building and running distributed applications with ease.

4. Core Architecture

Transformer (Attention is All You Need): The foundational architecture for most modern language models.
FlashAttention: An optimized attention mechanism for efficient computation.
Multi Query Attention: A variant that reduces attention computation costs.
Grouped Query Attention: Improves efficiency by grouping queries.
Google Titans: Models reportedly outperforming traditional Transformers in specific tasks.
VideoRoPE: Rotary positional encodings tailored for video data.

5. Mixture of Experts

Sparsely-Gated MoE Layer: A scalable layer that activates only a subset of expert models.
GShard: Google’s scalable training infrastructure for large models.
Switch Transformers: Uses a switch mechanism to route tokens through different expert modules efficiently.

6. RLHF (Reinforcement Learning from Human Feedback)

Deep RL with Human Feedback: Enhances language models by incorporating human feedback in training.
Fine-tuning LMs with RHLF: Improves model alignment and performance through iterative refinement.

7. Chain of Thought Reasoning

Chain-of-Thought Prompting: Guides models through multi-step reasoning processes.
Demystifying Long CoT Reasoning in LLMs: Research exploring how LLMs handle complex reasoning chains.

8. Reasoning

Transformer Reasoning Capabilities: Analysis and enhancement of Transformer-based reasoning.
Training LLMs in Latent Space: Investigates how training in continuous latent spaces boosts reasoning performance.

9. Optimizations

1-bit LLMs: Studies how to train and deploy ultra-low-precision language models.
FlashAttention-3: Further optimization of attention computation for large models.
Speculative Decoding: A decoding strategy that accelerates text generation.

10. Distillation

BYOL (Bootstrap Your Own Latent): A self-supervised learning method for representation learning.
DINO: Self-distillation without labels for training powerful visual models.

11. Structured State Space Models (SSMs)

RWKV: A hybrid model combining RNNs and Transformers.
Mamba: A high-performance model designed for long sequence modeling.

12. Image & Video Transformers

CLIP: Projects images and texts into the same embedding space for cross-modal retrieval.
ViViT: A pure-Transformer architecture for video classification.

13. Case Studies

Real-world applications from companies like Meta, OpenAI, Netflix, and more—offering insight into how AI technologies are applied in production settings.

Highlights of the Project

Practical Focus: Tailored for software engineers aiming to build real-world AI applications.
Comprehensive Scope: Covers foundational concepts, cutting-edge research, and applied tools.
Maintained by Experts: Actively maintained by Gaurav Sen, who continuously integrates new research and tools.
Community Recognition: With over 900 stars and 100+ forks on GitHub, it’s recognized as a valuable resource in the AI community.