Weights & Biases - Track Your ML Experiments Without Losing Your Mind

W&B exists because the Figure Eight team got sick of losing weeks of work to stupid shit like power outages and forgot-to-save-checkpoints disasters. Now 200,000+ ML engineers use it instead of crying into their keyboards at 3am.

The platform has two main parts: W&B Models for traditional ML (the stuff that actually works in production) and W&B Weave for LLM ops (because everyone's trying to build ChatGPT now). Both solve the same fundamental problem: keeping track of what the hell you did so you can do it again.

The "Oh Shit" Moment Prevention System

W&B logs your hyperparameters, metrics, and model artifacts automatically - no more "oh fuck, what learning rate did I use?" moments when your MacBook decides to install macOS Sequoia 15.1 in the middle of training. Captures loss curves, gradient norms, GPU utilization, and whatever custom metric you hacked together at 3am.

The experiment tracking catches the stuff you always forget: which learning rate actually worked, what preprocessing steps you used, and why this run performed 2% better than the last one. It's like version control for ML experiments, except it actually works and doesn't require a PhD in Git to understand.

Integration Reality Check

Adding W&B to your existing code takes literally 3 lines:

import wandb
wandb.init()
wandb.log({"loss": loss})

Works with PyTorch and TensorFlow - plus whatever else you need. Even handles the new PyTorch 2.x that broke some of my existing code. Unlike MLflow (which wants you to rewrite everything) or ClearML (which is basically malware disguised as an MLOps tool), W&B actually integrates with your existing spaghetti code.

W&B handles thousands of concurrent experiments without shitting itself, unlike that Flask app your intern built that crashes if more than one person logs in. You can run it cloud, on-prem, or in your own VPC - whatever keeps your CISO from having a panic attack about data sovereignty.

W&B Models handles traditional ML workflows - the bread and butter experiment tracking that keeps you from losing your mind when training deep networks. It's the stuff that was working fine before everyone got obsessed with ChatGPT clones.

Experiment Tracking That Doesn't Break

The experiment tracking logs everything automatically so you don't have to remember to save your hyperparameters at 2am. It captures loss curves, learning rates, gradient norms, and GPU utilization without you having to write custom logging code that breaks every other week.

Unlike that shitty logging script you wrote in 2019 that explodes at 1000 metrics, W&B handles millions of data points per run. The dashboard updates in real-time so you can watch your loss curve slowly converge (or spectacularly crater because you set the learning rate too high again) without refreshing TensorBoard like it's 2015.

Model Registry That Isn't a Glorified File Server

W&B Artifacts versions your models, datasets, and preprocessors together so you can actually reproduce results. It's not just dumping files in S3 with confusing names - it tracks lineage and dependencies so you know which dataset version broke your model's accuracy.

The model registry promotes models through dev/staging/prod without the "works on my machine" hell that ruined your last deployment. It hooks into CI/CD pipelines so only tested models hit production - no more "accidentally deployed the model that outputs garbage because I mixed up the feature columns" disasters at 5pm on Friday.

Hyperparameter Sweeps That Don't Bankrupt You

W&B Sweeps runs hyperparameter optimization that's smarter than grid search (which wastes 90% of your compute budget) and more reliable than random search (which is basically gambling with expensive GPUs).

The Bayesian optimization actually learns from previous runs instead of blindly trying every combination like an idiot. Early stopping kills bad runs before they waste 6 hours of A100 time, and the intelligent scheduling focuses compute on promising hyperparameter regions.

Unlike rolling your own hyperparameter search (which everyone tries once and regrets), Sweeps handles the distributed coordination, fault tolerance, and result aggregation without requiring a PhD in distributed systems.

W&B Weave tracks LLM applications so you can figure out why your OpenAI bill is $3000 this month and your supposedly "production-ready" RAG system is making shit up. It's experiment tracking for the "prompt engineering is real engineering" crowd who think adding "think step by step" fixes everything.

Anyway, here's what it actually does...

LLM Cost Tracking (Before You Go Bankrupt)

Weave traces every LLM call with token counts, latency, and cost so you can identify which prompts are burning money. Tracks the entire conversation flow - from your carefully crafted system prompt to the user's completely unhinged input to the model's response that somehow costs $2.50.

The tracing visualization shows you exactly which part of your RAG pipeline is expensive (spoiler: it's usually the retrieval step that pulls 50 irrelevant documents and feeds them all to GPT-4). For multi-agent workflows, it maps out which agent is making the most API calls and eating your budget.

Evaluations That Actually Test Edge Cases

W&B Evaluations runs systematic tests on your LLM applications instead of the usual "works on my laptop" evaluation methodology. It compares different prompts, models, and configurations to find what actually performs better on your specific use case.

The evaluation framework handles automated metrics (BLEU, ROUGE, whatever scores make you feel better) and human evaluations - because sometimes you need an actual human to tell you that your chatbot sounds like a condescending prick. You can A/B test prompts, compare GPT-4 vs Claude vs Llama 2 (good luck with that last one).

Production Monitoring (When Things Go Wrong at Scale)

W&B Guardrails blocks prompt injection attacks and filters toxic outputs before they reach users. It's like having a bouncer for your chatbot that kicks out problematic requests and responses.

W&B Monitors continuously evaluate your production LLM application and alert you when performance degrades. This catches issues like:

• Your model suddenly starting to refuse legitimate requests
• Response quality dropping after a model update
• Costs spiking because someone figured out how to game your system
• The classic "model outputs become unusable gibberish" scenario

Unlike hoping your users will report broken AI responses (they won't), Weave actively monitors and alerts you when things go sideways so you can fix them before your entire user base notices.

Big companies use W&B because their ML teams got tired of explaining to VPs why they can't reproduce the model that was "definitely working last month." Companies like OpenAI and Microsoft use it in production, so it probably won't shit itself when you move from 3 grad students to an actual team.

Real Companies Doing Real Work

Autonomous vehicle companies use W&B to track computer vision experiments because losing a week of training data when your self-driving car model fails is expensive and embarrassing. Financial firms use it for fraud detection models where "oops, we can't reproduce the model that catches credit card fraud" is a career-limiting move.

Healthcare and pharma companies run drug discovery and medical imaging models with W&B because regulatory compliance requires proving exactly how your model was trained. When the FDA asks "how did you train this diagnostic AI?", "uh, I think we used a learning rate of 0.001" isn't an acceptable answer.

Enterprise Security (So Your IT Team Stops Complaining)

The platform has SOC 2 Type II certification, HIPAA compliance options, and customer-managed encryption keys - basically all the checkboxes your security team needs to stop blocking the tool. You can run it on-premises or in your own VPC if you're paranoid about data leaving your environment.

SSO integration, role-based access controls, and SCIM user provisioning mean your IT department can manage users without manually creating accounts. Audit logs track who accessed what experiments, which is useful when someone accidentally deletes the model your entire product depends on.

Integration Reality

W&B works with AWS, GCP, Azure, and basically every ML framework that matters. The REST API lets you integrate with whatever internal tools you've built, assuming they don't suck.

The big news: CoreWeave acquired W&B in March 2025, closed in May. No price disclosed but probably cost more than most people's houses. Could mean better GPU integration and pricing, or it could mean the usual post-acquisition shitshow where everything gets worse and more expensive. Time will tell.

The 5-Minute Setup That Actually Takes 5 Minutes

Setup actually works without making you edit 37 config files. No YAML hell, no environment variable debugging, no "this only works on Ubuntu 18.04 with exactly these package versions" nonsense.

pip install wandb
wandb login  # Paste your API key from wandb.ai/authorize

Then add 3 lines to your training script:

import wandb
wandb.init(project="my-project")
wandb.log({"loss": loss, "accuracy": accuracy})

That's it. Your experiments are now tracked. The integration docs have copy-paste examples for PyTorch, TensorFlow, Hugging Face, Keras, and basically every framework that matters.

Learning Without the Bullshit

The quickstart tutorial actually works and doesn't assume you're an expert in distributed systems. The example projects have real working code you can run, not toy examples that break when you try to use them with real data.

The Discord community answers your questions faster than reading docs or opening support tickets. Real engineers debugging real problems at 3am, not "thought leaders" discussing MLOps governance frameworks that sound great in meetings but break in production.

When You Need More Help

The Fully Connected blog has technical posts from practitioners who've actually used W&B in production. They include the gotchas, failure modes, and "here's what we learned the hard way" insights you won't find in official documentation.

YouTube tutorials focus on practical implementation rather than theoretical frameworks. Their technical videos actually teach you something without conference talk filler or buzzword overload.

For enterprise teams, support actually responds and helps instead of sending you through 47 layers of documentation you've already read. The customer success people understand ML engineering problems rather than generic SaaS support. They also provide onboarding assistance and best practices consulting for teams scaling their ML workflows.