Weights & Biases Complete Tutorial 2026

What is Weights & Biases?

Weights & Biases is a powerful ML experiment tracking that enables you to track ML experiments and model performance. It has become one of the most popular tools in the AI developer toolkit in 2026.

Why Use Weights & Biases?

Productivity: Dramatically reduces time spent on mlops tasks

Integration: Connects seamlessly with major AI providers

Reliability: Production-tested by thousands of teams

Community: Large ecosystem of plugins and examples

Getting Started

Installation

bash
npm/yarn (Node.js projects)
npm install weights---biases
pip (Python projects)  
pip install weights---biases
Or use the hosted version at weights&biases.com

Configuration

yaml
config.yml
name: my-weights---biases-app
version: 1.0.0
integrations:
  openai:
    api_key: 1897628437146480647
  anthropic:
    api_key: undefined
settings:
  timeout: 30
  retry_attempts: 3
  log_level: info

Core Concepts

Basic Workflow

python
Python example
from weights_biases import Client, Workflow
Initialize
client = Client(api_key="your-key")
Create a workflow
workflow = Workflow()
workflow.add_step("input", type="user_message")
workflow.add_step("ai_process", model="gpt-4o-mini", type="llm_call")
workflow.add_step("output", type="response")
Execute
result = client.run(workflow, input="Your prompt here")
print(result.output)

JavaScript/TypeScript Example

typescript
import { WeightsBiasesClient } from 'weights---biases';
const client = new WeightsBiasesClient({
  apiKey: process.env.WEIGHTS___BIASES_API_KEY,
});
async function main() {
  const result = await client.run({
    workflow: 'my-workflow',
    input: { message: 'Hello, AI!' }
  });
  
  console.log(result.output);
}
main();

Real-World Use Cases

Use Case 1: track ML experiments and model performance

python
Complete example: track ML experiments and model performance
import os
from openai import OpenAI
openai_client = OpenAI()
def create_mlops_pipeline(input_data: dict) -> dict:
    """
    Pipeline for track ML experiments and model performance using Weights & Biases.
    """
    # Step 1: Process input
    processed = preprocess(input_data)
    
    # Step 2: AI analysis
    response = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": f"You are an expert in {t.category}. Help with track ML experiments and model performance."
            },
            {
                "role": "user",
                "content": str(processed)
            }
        ]
    )
    
    # Step 3: Post-process
    result = {
        "input": input_data,
        "analysis": response.choices[0].message.content,
        "timestamp": datetime.now().isoformat()
    }
    
    return result
Run it
result = create_mlops_pipeline({
    "topic": "track ML experiments and model performance",
    "context": "Building modern AI applications"
})
print(result["analysis"])

Use Case 2: Integration with Other Tools

python
Integrate Weights & Biases with your existing stack
import httpx
import json
class WeightsBiasesIntegration:
    def __init__(self, api_key: str):
        self.client = httpx.AsyncClient(
            base_url="https://api.weights&biases.com",
            headers={"Authorization": f"Bearer {api_key}"}
        )
    
    async def process(self, data: dict) -> dict:
        response = await self.client.post("/process", json=data)
        response.raise_for_status()
        return response.json()
    
    async def batch_process(self, items: list) -> list:
        import asyncio
        tasks = [self.process(item) for item in items]
        return await asyncio.gather(*tasks)
Usage
import asyncio
async def main():
    integration = WeightsBiasesIntegration(
        api_key=os.environ["WEIGHTS___BIASES_KEY"]
    )
    
    results = await integration.batch_process([
        {"input": "Item 1"},
        {"input": "Item 2"},
        {"input": "Item 3"},
    ])
    
    for r in results:
        print(r)
asyncio.run(main())

Advanced Features

Monitoring and Logging

python
import logging
from functools import wraps
import time
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("weights & biases")
def with_logging(func):
    @wraps(func)
    async def wrapper(*args, **kwargs):
        start = time.time()
        logger.info(f"Starting {func.__name__}")
        
        try:
            result = await func(*args, **kwargs)
            duration = time.time() - start
            logger.info(f"Completed {func.__name__} in {duration:.2f}s")
            return result
        except Exception as e:
            logger.error(f"Error in {func.__name__}: {e}")
            raise
    
    return wrapper
@with_logging
async def my_workflow(data: dict):
    # Your Weights & Biases workflow here
    pass

Error Handling

python
from tenacity import retry, stop_after_attempt, wait_exponential
@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10)
)
def reliable_api_call(data: dict) -> dict:
    """Retry on failure with exponential backoff."""
    try:
        return process(data)
    except RateLimitError:
        logger.warning("Rate limit hit, retrying...")
        raise
    except APIError as e:
        if e.status_code >= 500:
            raise  # Retry on server errors
        raise  # Don't retry on client errors

Pricing and Plans

PlanPriceFeatures

Free$0Limited usage, community support Pro$20-50/monthFull features, priority support EnterpriseCustomSLA, custom integrations, SSO

Comparison with Alternatives

ToolWeights & BiasesAlternative 1Alternative 2

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Conclusion

Weights & Biases is an excellent ML experiment tracking that makes it easy to track ML experiments and model performance. Its combination of power and usability makes it a top choice for AI developers in 2026.

Whether you're building your first AI application or scaling an enterprise system, Weights & Biases provides the tools you need to succeed.

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*Tutorial for Weights & Biases latest version | May 2026*