AI / ML Engineering

Models from first principles up to transformers, RAG and agents — then the serving, evaluation and monitoring that keep them alive in production.

• Linear Regression
• Gradient Descent
• Logistic Regression
• Bias–Variance Tradeoff
• Decision Tree
• K-Means Clustering
• Principal Component Analysis
• Neural Network Forward Pass
• Backpropagation
• BPE Tokenization
• Self-Attention
• RAG Pipeline
• Random Forest
• Gradient Boosting / XGBoost
• Embedding Vector Space
• Transformer Block
• KV Cache
• LoRA / QLoRA
• Hybrid Retrieval + Reranking
• Corrective RAG
• Agent Tool Calling
• LangGraph Workflow
• LLMOps Monitoring
• Model Routing & Token Cost
• Confusion Matrix & Metrics
• ROC Curve & AUC
• Activation Functions
• Token Sampling (Temperature, Top-k, Top-p)
• CNN Convolution
• Cross-Validation
• RLHF & DPO (Alignment)
• Prompt Injection & Guardrails
• Agentic RAG on AWS
• HNSW Vector Index
• How a Vector Database Works
• Cross-Encoder Reranking
• Chain-of-Thought & Self-Consistency
• Mixture of Experts (MoE)
• Agentic RAG on GCP
• Transformer Architecture
• One-Hot Encoding
• Regularization
• Feature Scaling
• Optimizers
• Vanishing Gradient
• K-Nearest Neighbors
• Naive Bayes
• Support Vector Machine
• Ensemble Methods
• t-SNE
• LLM as a Judge
• Tokenization
• CLIP Alignment
• Recurrent Neural Network
• Vision Transformer (ViT)
• Multi-Head Attention
• Attention Masking
• Cross Attention
• Flash Attention
• Grouped Query Attention
• Layer Norm
• Positional Encoding
• RoPE Positional Encoding
• Context Window
• GPT Generation Loop
• Speculative Decoding
• Structured Output
• Zero-shot vs One-shot vs Few-shot
• Citations & Grounding
• GraphRAG
• BM25 / TF-IDF
• Chunking Strategies
• Semantic Cache
• ReAct Agent
• Agent Memory
• Agent Planning
• Agent Reflection
• Multi-Agent Systems
• MCP Protocol
• Diffusion Denoising
• Instruction Tuning
• Quantization
• Distributed Training
• Multistage Ranking
• Two Tower
• Data Drift
• Feature Store
• Model Monitoring
• Shadow Deployment
• Training-Serving Skew
• OpenAI Agents SDK
• Claude Agent SDK
• Browser Agents
• Decorators
• Generators
• Async / Await
• Context Managers
• Type Hints
• Comprehensions
• Pydantic
• FastAPI
• SELECT & Filtering
• SQL Joins
• GROUP BY & Aggregates
• SQL Indexes
• Subqueries & CTEs
• Window Functions
• Transactions & ACID
• AWS S3
• AWS SageMaker
• AWS Bedrock
• GCP Cloud Storage
• Vertex AI
• BigQuery
• Mean, Median & Mode
• Variance & Std Dev
• Probability Basics
• Bayes' Theorem
• Distributions
• Expectation & Variance
• Correlation & Covariance
• Hypothesis Testing
• LangChain
• LangSmith
• Langfuse
• YOLO Object Detection
• Knowledge Graphs (Neo4j)
• Matryoshka Embeddings
• Late Interaction (ColBERT)
• Learned Sparse Retrieval (SPLADE)
• Fine-tuning Embeddings
• Product Quantization (IVF-PQ)
• Binary Quantization + Rescoring
• Filtered Vector Search
• DiskANN & Billion-Scale Search
• Contextual Retrieval
• RAPTOR Hierarchical Index
• HyDE & Query Expansion
• Self-RAG & Reflective Retrieval
• Agentic RAG Controller
• PagedAttention & Continuous Batching
• Prompt Caching
• Constrained Decoding
• Test-Time Compute & Reasoning Models
• Long-Context Scaling
• Mixture of Agents
• Agent Sandboxing
• Deep Research Agents
• OLTP vs OLAP
• Star Schema
• Normalization vs Denormalization
• Columnar Storage & Parquet
• Partitioning & Clustering
• Lake, Warehouse & Lakehouse
• Batch vs Streaming
• ETL vs ELT
• Orchestration & DAGs
• Idempotency & Backfills
• dbt & the Transformation Layer
• Data Quality Tests
• Schema Evolution & Data Contracts
• Slowly Changing Dimensions
• Kafka & the Commit Log
• Change Data Capture
• Event Time & Watermarks
• Windowing & Streaming Aggregation
• Delivery Semantics
• Spark's Execution Model
• The Shuffle
• Joins at Scale
• Data Skew
• Predicate Pushdown & Query Planning
• Data Observability
• Lineage & Impact Analysis
• Warehouse Cost & Performance
• Governance & PII
• Pipeline Failure & On-Call
• From Question to Metric
• Sampling & Selection Bias
• Correlation vs Causation
• Simpson's Paradox
• Cohort Analysis
• Funnel Analysis
• Retention Curves
• Running Totals & Moving Averages
• Defining a Metric
• Metric Trees
• DAU, MAU & Stickiness
• Unit Economics: LTV & CAC
• Guardrail Metrics
• Anatomy of an A/B Test
• p-values & Significance
• Confidence Intervals
• Power & Sample Size
• Sample Ratio Mismatch
• Choosing the Right Chart
• Dashboard Design
• Telling the Story
• The Semantic Layer
• Loop Engineering
• Context Engineering
• Tool Design
• Memory Architecture
• Orchestration Patterns
• Guardrails & Permissions
• Evals for Agents
• Human-in-the-Loop Design
• Observability & Tracing