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MLOps & Production

Model deployment, CI/CD for ML, monitoring, Docker, Kubernetes, SageMaker, and end-to-end ML pipelines.

110 concepts3 questions22 projects

Overview

MLOps (Machine Learning Operations) bridges the gap between ML development and production deployment. It applies DevOps principles to the ML lifecycle, ensuring models are reliably deployed, monitored, and maintained at scale.

Key areas include model serving (REST APIs, batch inference, real-time vs. batch, model registries), CI/CD for ML (automated training pipelines, model validation gates, A/B testing), containerization (Docker, Kubernetes for scaling), and cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML).

Production concerns include monitoring (data drift, model degradation, concept drift), feature stores (offline and online feature serving), experiment tracking (MLflow, Weights & Biases), and infrastructure (GPU cluster management, cost optimization). MLOps is where most ML projects fail — building the model is 10% of the work; deploying and maintaining it is the other 90%.

MLOps & Production

Overview

Deep-Dive Concepts (from Projects)

Transfer Learning with BERT

Model Quantization for Production

Evaluation Metrics Beyond Accuracy

FastAPI for ML Model Serving

Transfer Learning in Computer Vision

Model Optimization: ONNX and Quantization

Grad-CAM: Model Interpretability

FastAPI for ML Model Serving

Matrix Factorization Deep Dive

Two-Stage Recommendation Architecture

Recommendation Evaluation Metrics

Cold-Start Problem Solutions

Diversity in Recommendations

Implicit Feedback in Recommendations

How Embeddings Work

Vector Similarity Explained

Chunking Strategies Compared

Two-Stage Retrieval Architecture

Prompt Engineering for RAG

Evaluating RAG Systems

Multi-Agent Architecture

LangGraph State Machines

Agent Communication Patterns

Tool Integration and Error Handling

LangSmith Observability: Complete Guide

Production Optimization

Model Context Protocol (MCP) Explained

AI Voice Agent Architecture

Tool Orchestration in Multi-Tool AI Systems

Webhook Design for AI Agents

Client-Side Tool Integration for Voice Agents

Production Voice Agent Deployment

Multi-Modal AI Pipelines

Prompt Engineering for Creative AI

Character Consistency in AI Art

Server-Sent Events (SSE)

Professional PDF Generation

AI Content Safety

AWS Bedrock Architecture Deep-Dive

RAG with Bedrock Knowledge Bases

Content Safety with Bedrock Guardrails

Gemini 1.5's 2M Token Context Window

Multi-Cloud AI Abstraction Patterns

Azure OpenAI Service Architecture

Three-Cloud Decision Framework

Cost Optimization Strategies for Cloud AI

QLoRA: Quantized Low-Rank Adaptation

vLLM Inference Optimization

TGI: Text Generation Inference

FSDP: Fully Sharded Data Parallel

DeepSpeed ZeRO: Zero Redundancy Optimizer

Multi-Node Training Networking

PPO: Proximal Policy Optimization

Reward Shaping for Trading

Parallelism Taxonomy

Chinchilla Scaling Laws

BM25 Algorithm

Inverted Index

HNSW Algorithm

Hybrid Search Fusion

Two-Stage Retrieval

Search Evaluation Metrics

Query Understanding

RAG Architecture

The A2A Protocol Architecture

Agent Discovery and Capability Negotiation

Document Processing Pipelines

Multi-Agent vs Single Agent Systems

Verification Confidence Scoring

Server-Sent Events (SSE)

Fine-Tuning: What, Why, and How

LoRA (Low-Rank Adaptation)

QLoRA (Quantized LoRA)

Tokens and Tokenization

Temperature and Sampling

Embeddings and Vector Search

RAG (Retrieval Augmented Generation)