Ehsan Jahandarpour
Tool Stack

Scale AI + Weights & Biases: ML Development Stack

Scale AI handles data labeling and curation while Weights & Biases tracks experiments and models. The complete ML development cycle: label data (Scale) → train and track (W&B) → evaluate (Scale) → iterate.

Tools in This Stack

Scale AI

Enterprise AI data infrastructure for labeling, evaluation, and synthetic data

Weights & Biases

MLOps platform for experiment tracking

Setup Guide

  1. 1
    Scale AI account

    Custom pricing based on data volume and labeling complexity.

  2. 2
    W&B account

    Free for personal use. Team at $50/user/mo.

  3. 3
    Integration code

    Add wandb SDK to training pipeline — 3 lines of code.

  4. 4
    Evaluation pipeline

    Set up automated evaluation runs triggered by model checkpoint saves.

Integration Steps

  1. 1
    Label data with Scale

    Send raw data to Scale AI for human-in-the-loop labeling with quality checks.

  2. 2
    Train with W&B tracking

    Use wandb.init() in training scripts to log metrics, hyperparameters, and artifacts.

  3. 3
    Evaluate with Scale

    Use Scale's evaluation platform to benchmark model performance on labeled test sets.

  4. 4
    Iterate based on insights

    W&B experiment comparison reveals which changes improved metrics. Scale relabels edge cases.

Cost Analysis

ItemCost
Total$1,250-5,250/mo for 5-person ML team
Scale AICustom (typically $1-5K/mo)
W&B Team$50/user/mo

Ehsan's Recommendation

ML teams waste 80% of their time on data issues, not model architecture. This stack attacks both: Scale ensures data quality, W&B ensures experiment reproducibility. Together they cut the "we cannot reproduce last week's results" problem that plagues every ML team I have worked with.

Alternative Stacks

Labelbox + MLflowScale + Neptune
EJ

Ehsan Jahandarpour

AI Growth Strategist & Fractional CMO

Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council

Frequently Asked Questions

How do scale ai and weights and biases work together?
Scale AI handles data labeling and curation while Weights & Biases tracks experiments and models. The complete ML development cycle: label data (Scale) → train and track (W&B) → evaluate (Scale) → iterate.
How much does this stack cost?
Total estimated cost: $1,250-5,250/mo for 5-person ML team. Scale AI: Custom (typically $1-5K/mo). W&B Team: $50/user/mo.
What are the alternatives to this stack?
Alternative stacks include: Labelbox + MLflow, Scale + Neptune.