Enterprise AI Engineering Handbook
40 haystack pipelines and components
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Part I - Machine Learning
Part II - Deep Learning
Part III - Foundation Models, Large Language Models & Generative AI
Part IV - Prompt Engineering & RAG Fundamentals
Part V - Advanced Retrieval-Augmented Generation
Part VI - AI Agents
Part VII - Agentic AI & Multi-Agent Systems
Part VIII - AI Engineering Frameworks & Tooling
Part IX - MLOps, LLMOps & AIOps
Part X - Cloud AI Engineering
Part XI - Enterprise AI Architecture & Design Patterns
Part XII - Interview Preparation
Enterprise AI Engineering Handbook
MihirJha/Enterprise-AI-Engineering-Handbook
Home
Part I - Machine Learning
Part I - Machine Learning
Machine Learning Fundamentals
Machine Learning Fundamentals
01-Introduction to Machine Learning
02-Machine Learning Fundamentals
03-Machine Learning Lifecycle
04-Machine Learning in Practice
05-Machine Learning Ecosystem and Tools
Regression
Regression
01-Regression Fundamentals
02-Linear Regression
03-Nonlinear Regression
04-Logistic Regression
05-Training and Evaluating Regression Models
Classification
Classification
01-Classification Fundamentals
02-Decision Trees
03-Regression Trees
04-Support Vector Machines
05-K-Nearest Neighbors
06-Classification Model Evaluation
07-Feature Scaling and Data Preparation
08-Bias-Variance Tradeoff
09-Ensemble Learning
10-Building Production Classification Systems
Unsupervised Learning
Unsupervised Learning
01-Unsupervised Learning Fundamentals
02-Clustering Fundamentals
03-K-Means Clustering
04-Density-Based Clustering
05-Hierarchical Clustering
06-Dimensionality Reduction Fundamentals
07-Principal Component Analysis (PCA)
08-t-SNE and UMAP
09-Clustering for Feature Engineering
10-Building Production Unsupervised Learning Systems
Model Evaluation & Generalization
Model Evaluation & Generalization
01-Model Evaluation Fundamentals
02-Classification Evaluation Metrics
03-Regression Evaluation Metrics
04-Unsupervised Learning Evaluation
05-Cross-Validation and Model Validation
06-Regularization Techniques
07-Data Leakage and Modeling Pitfalls
Part II - Deep Learning
Part II - Deep Learning
Deep Learning Fundamentals
Deep Learning Fundamentals
01-Introduction to Deep Learning
02-Neural Network Fundamentals
03-Shallow and Deep Neural Networks
04-Linear and Logistic Regression
05-Activation Functions, Probabilities and Thresholding
06-Loss Functions and Maximum Likelihood
07-Forward and Backpropagation
08-Gradient Descent and Mini-Batch Training
09-Weight Initialization and Gradient Stability
10-Regularization and Generalization
11-Advanced Optimization Techniques
12-Hyperparameter Tuning and Training Strategies
Deep Learning Frameworks
Deep Learning Frameworks
01-TensorFlow and Keras Fundamentals
02-Keras Sequential and Functional API
03-Custom Layers, Models and Training Loops
04-PyTorch Fundamentals and Tensors
05-PyTorch Autograd, Dataset and DataLoader
06-Building Classification and Regression Models
Computer Vision & CNNs
Computer Vision & CNNs
01-Convolutional Neural Networks
02-CNN Architecture, Optimization and Training
03-Transfer Learning and Fine-Tuning
04-ResNet, Residual Connections and Torchvision
05-Vision Transformers and CNN-ViT Hybrids
Sequence Models & Transformers
Sequence Models & Transformers
01-Recurrent Neural Networks
02-LSTM and GRU
03-Attention and Positional Encoding
04-Transformer Architecture
05-Transformer Applications
Generative Deep Learning
Generative Deep Learning
01-Autoencoders and Representation Learning
02-Generative Adversarial Networks
03-Diffusion Models
Reinforcement Learning
Reinforcement Learning
01-Reinforcement Learning Fundamentals
02-Markov Decision Processes and Q-Learning
03-Deep Reinforcement Learning and DQN
Production Deep Learning
Production Deep Learning
01-GPU-Accelerated Deep Learning
02-Deep Learning Training and Model Lifecycle
03-Building Production Deep Learning Systems
Part III - Foundation Models, Large Language Models & Generative AI
Part III - Foundation Models, Large Language Models & Generative AI
Foundation Models & LLM Fundamentals
Foundation Models & LLM Fundamentals
01-Generative AI Fundamentals
02-Language Understanding Fundamentals
03-Word Embeddings
04-Language Modeling
05-Attention & Positional Encoding
06-GPT & BERT Architecture
07-Hugging Face & Transformers
LLM Training & Fine-Tuning
LLM Training & Fine-Tuning
01-LLM Data Preparation
02-Hugging Face Training Workflow
03-Transformer Fine-Tuning Fundamentals
04-Supervised Fine-Tuning (SFT)
05-Parameter-Efficient Fine-Tuning
06-LoRA & QLoRA
07-Model Quantization
LLM Generation & Evaluation
LLM Generation & Evaluation
01-LLM Generation Strategies
02-LLM Evaluation
LLM Alignment & Preference Optimization
LLM Alignment & Preference Optimization
01-Instruction Tuning
02-Reward Modeling
03-LLMs as Policies
04-Reinforcement Learning from Human Feedback
05-Proximal Policy Optimization (PPO)
06-Direct Preference Optimization (DPO)
07-Hugging Face TRL Workflow
Part IV - Prompt Engineering & RAG Fundamentals
Part IV - Prompt Engineering & RAG Fundamentals
Prompt Engineering & LLM Interaction
Prompt Engineering & LLM Interaction
01-Introduction to Prompt Engineering
02-Prompt Engineering Fundamentals
03-Advanced Prompt Engineering
04-Prompt Design Patterns
05-Zero, One & Few-Shot Prompting
06-Chain-of-Thought Prompting
07-ReAct Prompting
08-Structured Outputs & Output Parsing
09-Function Calling & Tool Calling
Embeddings & Vector Search
Embeddings & Vector Search
01-Embeddings in Practice
02-Document Processing & Vectorization
03-Document Chunking Strategies
04-Vector Database Fundamentals
05-Similarity Search Techniques
RAG Fundamentals
RAG Fundamentals
01-RAG Pipeline Components
02-Retrieval & Generation Pipeline
03-Vector Databases in RAG
04-Building Your First RAG Pipeline
05-RAG Evaluation Fundamentals
Enterprise AI Application & Deployment
Enterprise AI Application & Deployment
01-Enterprise Generative AI Application Architecture
02-Deploying AI Applications with Gradio
03-Deploying AI Applications with Flask
Part V - Advanced Retrieval-Augmented Generation
Part V - Advanced Retrieval-Augmented Generation
Core Retrieval Engineering
Core Retrieval Engineering
01-vectostore-retriever
02-multi-query-retriever
03-self-query-retriever
04-parent-document-retriever
05-retriever-comparison
Enterprise Retrieval Engineering
Enterprise Retrieval Engineering
01-contextual-compression-retriever
02-ensemble-retriever
03-multivector-retriever
04-timeweighted-retriever
05-hybrid-search-retriever
06-hyde-retriever
07-router-retriever
08-multi-stage-retrieval
09-agentic-retrieval
10-reranking-techniques
11-mmr-and-diversity-aware-retrieval
12-metadata-aware-retrieval
13-advanced-query-rewriting
LlamaIndex Retrieval Engineering
LlamaIndex Retrieval Engineering
01-llamaindex-retrievers-overview
02-llamaindex-indexes
03-vector-index-retriever
04-bm25-retriever
05-document-summary-retriever
06-recursive-retriever
07-query-fusion-retriever
08-auto-merging-retriever
Vector Search Engineering
Vector Search Engineering
01-faiss-fundamentals
02-faiss-indexes
03-ivf-and-hnsw
04-faiss-vs-chromadb-vs-milvus
Advanced RAG Architecture
Advanced RAG Architecture
01-advanced-rag-architecture
02-graph-rag
03-knowledge-graphs-for-rag
04-sql-rag
05-multimodal-rag
06-agentic-rag
Production RAG Engineering
Production RAG Engineering
01-prompt-assembly
02-context-selection-and-context-engineering
03-response-validation
04-citation-and-source-attribution
05-enterprise-response
06-rag-evaluation-and-benchmarking
07-rag-observability
08-rag-performance-optimization
09-rag-cost-optimization
10-production-retrieval-architecture
11-building-production-rag-systems
12-rag-deployment-patterns
13-rag-caching-strategies
14-multi-tenant-rag
15-rag-testing-frameworks
16-rag-failure-patterns
Part VI - AI Agents
Part VI - AI Agents
AI Agent Fundamentals
AI Agent Fundamentals
01-ai-agent-fundamentals
02-tool-calling-and-function-calling
03-building-and-orchestrating-tools
04-lcel-and-manual-tool-calling
05-langchain-built-in-agents
06-ai-agent-design-best-practices
07-enterprise-ai-agent-architecture
08-planning-and-task-decomposition
09-agent-reasoning
10-reflection-and-self-correction
Agent Memory
Agent Memory
01-agent-memory-overview
02-short-term-memory
03-long-term-memory
04-working-memory
05-episodic-memory
06-semantic-memory
07-memory-storage-patterns
08-memory-retrieval-patterns
09-memory-compression
10-memory-optimization
Agent Communication
Agent Communication
01-agent-communication-overview
02-message-passing
03-shared-memory
04-event-driven-agents
05-publish-subscribe-pattern
06-agent-coordination
07-agent-negotiation
08-conflict-resolution
Agent Observability
Agent Observability
01-agent-observability-overview
02-agent-logging
03-agent-tracing
04-agent-monitoring
05-agent-metrics
06-agent-debugging
07-agent-evaluation-metrics
08-agent-cost-monitoring
09-agent-alerting
10-agent-evaluation
Agent Security
Agent Security
01-agent-security-overview
02-prompt-injection
03-tool-security
04-agent-authentication
05-agent-authorization
06-secrets-management
07-data-privacy
08-agent-sandboxing
09-agent-guardrails
10-agent-risk-management
Agent Deployment
Agent Deployment
01-agent-deployment-overview
02-agent-runtime-and-execution
03-agent-scaling-and-resilience
04-production-agent-deployment
Part VII - Agentic AI & Multi-Agent Systems
Part VIII - AI Engineering Frameworks & Tooling
Part VIII - AI Engineering Frameworks & Tooling
LangChain
LangChain
01-langchain-fundamentals
02-langchain-models-and-prompts
03-langchain-tools-and-function-calling
04-langchain-retrieval-and-rag
05-langchain-memory-and-state
06-langchain-agents
07-langchain-production-patterns
08-langchain-limitations-and-tradeoffs
LlamaIndex
LlamaIndex
01-llamaindex-fundamentals
02-llamaindex-data-and-document-ingestion
03-llamaindex-indexes-and-retrieval
04-llamaindex-rag-pipelines
05-llamaindex-agents-and-tools
06-llamaindex-workflows
07-llamaindex-production-patterns
08-llamaindex-limitations-and-tradeoffs
LangGraph
LangGraph
01-langgraph-fundamentals
02-graph-based-agent-architecture
03-langgraph-state-and-checkpointing
04-langgraph-nodes-edges-and-routing
05-langgraph-human-in-the-loop
06-langgraph-tool-execution
07-langgraph-agent-workflows
08-langgraph-memory-and-persistence
09-langgraph-production-patterns
10-langgraph-limitations-and-tradeoffs
Part IX - MLOps, LLMOps & AIOps
Part X - Cloud AI Engineering
Part XI - Enterprise AI Architecture & Design Patterns
Part XII - Interview Preparation
40 haystack pipelines and components
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