ML Training Stack

A machine learning training stack is a software architecture that prepares data, trains models, evaluates performance, and manages the computational workflows required to develop machine learning systems. These architectures support recommendation systems, computer vision, language models, forecasting systems, robotics, scientific machine learning, and enterprise AI platforms.

Typical Architecture

A common machine learning training architecture looks like this:

Datasets
    ↓
Data Pipelines
    ↓
Training Orchestration
    ↓
Compute Infrastructure
    ↓
Model Training + Evaluation
    ↓
Experiment Tracking

Simple Architecture

A minimal machine learning training stack may include:

Dataset
Training Script
Compute Instance
Model Checkpoints
Basic Logging

Production Architecture

A larger production deployment may include:

Distributed Data Pipelines
Dataset Versioning
Training Orchestration
GPU / TPU Clusters
Distributed Storage
Checkpoint Management
Experiment Tracking
Hyperparameter Optimization
Model Evaluation Pipelines
Workflow Automation
Monitoring Infrastructure
Cluster Scheduling
Feature Stores
Simulation Environments
AI-Assisted Optimization