Reference 3 stops to get here

FLOPS

Floating Point Operations Per Second - a measure of computational performance, used to quantify training and inference costs.

Your route here

3 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Compute ✓ understood

    Informal term for computational resources (GPUs, TPUs, time) required for training or running AI models.

  4. FLOPS · you are here ✓ understood

Where it sits

Before this

Compute
FLOPS

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Shipping AI GPU Graphics Processing Unit - hardware accelerator with thousands of cores, essential for parallel computation in deep learning. Shipping AI TPU Tensor Processing Unit - Google's custom hardware accelerator designed specifically for machine learning workloads. Language & LLMs Neural Scaling Laws Empirical relationships showing how model performance improves predictably with model size, data, and compute. Shipping AI Throughput The number of predictions or tokens a model can process per unit of time, a key deployment performance metric. Training Mixed Precision Training Using lower precision (FP16) for some computations while keeping FP32 for stability, speeding up training.