ML System Design

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What is Machine Learning?

What is ML System Design?

ML vs Traditional System Design

Supervised vs Unsupervised Learning

Classification vs Regression

Common ML Algorithms

Embeddings and Representation Learning

Loss Functions and Optimization

Bias-Variance Tradeoff

Model Selection

Framing ML Problems

Data Collection Strategies

Data Labeling at Scale

Data Sampling and Augmentation

Feature Engineering

Data Validation & Quality

Handling Data Skew and Imbalance

Training Pipelines

Distributed Training

Transfer Learning and Fine-Tuning

Hyperparameter Tuning

Experiment Tracking

Model Versioning

Evaluation Metrics

Offline Evaluation

Online Evaluation

A/B Testing for ML

Model Fairness & Bias

Model Deployment Strategies

Batch vs Real-time Inference

Model Optimization

Serving Infrastructure

Feature Serving

Scaling ML Systems

Cost Optimization for ML

Model Monitoring

Data & Model Drift Detection

Model Retraining Strategies

Embedding-Based Retrieval

Two-Tower Architecture

Multi-Stage Ranking

Ensemble Methods

Knowledge Distillation

Online Learning and Continual Training

Active Learning

ML System Design

Welcome

Course Roadmap
Join the Community

Introduction

What is Machine Learning?
What is ML System Design?
ML vs Traditional System Design

ML Fundamentals

Supervised vs Unsupervised Learning
Classification vs Regression
Common ML Algorithms
Embeddings and Representation Learning
Loss Functions and Optimization
Bias-Variance Tradeoff
Model Selection
Framing ML Problems

Data Engineering for ML

Data Collection Strategies
Data Labeling at Scale
Data Sampling and Augmentation
Feature Engineering
Feature Stores
Data Pipelines
Data Validation & Quality
Handling Data Skew and Imbalance

Model Training

Training Pipelines
Distributed Training
Transfer Learning and Fine-Tuning
Hyperparameter Tuning
Experiment Tracking
Model Versioning

Model Evaluation

Evaluation Metrics
Offline Evaluation
Online Evaluation
A/B Testing for ML
Model Fairness & Bias

Model Serving and Infrastructure

Model Deployment Strategies
Batch vs Real-time Inference
Model Optimization
Serving Infrastructure
Model Caching
Feature Serving
Scaling ML Systems
Cost Optimization for ML

MLOps

ML Pipelines
Model Monitoring
Data & Model Drift Detection
Model Retraining Strategies
CI/CD for ML

ML Design Patterns

Embedding-Based Retrieval
Two-Tower Architecture
Multi-Stage Ranking
Cascade Models
Ensemble Methods
Knowledge Distillation
Online Learning and Continual Training
Active Learning

Data & Model Drift Detection

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