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Cloud & Infrastructure

AWS, GCP, cloud services, serverless computing, and infrastructure for ML workloads.

62 concepts2 questions10 projects

Overview

Cloud infrastructure is the backbone of modern ML systems. Understanding cloud services and how to leverage them effectively is essential for deploying, scaling, and managing ML workloads in production.

Key areas include compute (EC2, Lambda, ECS, GKE for training and serving), storage (S3, GCS for data lakes, model artifacts), ML-specific services (SageMaker, Vertex AI, Bedrock for managed ML), and networking/security (VPCs, IAM roles, API Gateway).

Important concepts include choosing between managed services vs. self-hosted solutions, cost optimization (spot instances, autoscaling, right-sizing), infrastructure as code (Terraform, CloudFormation), and multi-cloud strategies. Understanding these services helps you design ML systems that are cost-effective, scalable, and production-ready.

Cloud & Infrastructure

Overview

Deep-Dive Concepts (from Projects)

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

PPO: Proximal Policy Optimization

Reward Shaping for Trading

MCP Protocol Deep Dive

Copilot Studio Architecture

Power Platform Security Model

AI Builder Capabilities

Dataverse for AI Workloads

Low-Code AI Governance

Parallelism Taxonomy

Chinchilla Scaling Laws

BM25 Algorithm

Inverted Index

HNSW Algorithm

Hybrid Search Fusion

Two-Stage Retrieval

Search Evaluation Metrics

Query Understanding

RAG Architecture

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)

Prompt Injection and LLM Security

Context Window and Attention

LoRA vs Full Fine-Tuning

Feature Store Architecture

Spot Training Economics

Multi-Model Endpoints

Model Monitor Metrics

SageMaker Pipelines vs Step Functions

Serverless Inference Trade-offs

GPU Instance Selection

Clarify Bias Metrics

SageMaker vs Self-Hosted MLOps

Distributed Training Strategies

MLOps Platform Comparison: SageMaker vs Vertex AI vs Kubeflow vs Databricks

NLU Pipeline: From Text to Intent

Containment Rate: The Key CCAI Metric

XGBoost for Tabular Classification

SHAP Values and Model Explainability

Palantir Foundry Ontology Architecture

Star Schema Design for Analytics

Cost-Sensitive Classification

Feature Engineering for Behavioral Data