Developer Focused MLOps on AWS

Build better models faster with Weights & Biases, the leading developer-first MLOps platform

Unlock your machine learning team’s productivity by optimizing, visualizing, collaborating on, and standardizing their model and data pipelines – regardless of framework, environment, or workflow. Weights & Biases streamlines MLOps for machine learning and deep learning workloads on AWS, whether on Amazon SageMaker or running your own machine learning on AWS infrastructure.
Explore the Weights & Biases platform






Dataset and model versioning


Interactive data visualization


Hyperparameter optimization

Smart farming with BlueRiver Technologies

John Deere subsidiary, BlueRiver Technologies, leverages Weights & Biases MLOps to accelerate building computer vision models for weed detection for targeted crop spraying.

# Flexible integration for any Python script

import wandb

# 1. Start a W&B run


# 2. Save model inputs and hyperparameters

config = wandb.config
config.learning_rate = 0.01

# Model training here

# 3. Log metrics over time to visualize performance

wandb.log({“loss“: loss})

Integrate with W&B with a few lines of code

Streamline your MLOps for your machine learning workloads on Amazon SageMaker. Users can integrate Weights & Biases with just a couple lines of Python code.

Use W&B with SageMaker

We made Weights & Biases to be flexible and easy to use, regardless of your framework or workflow. Click the link below to read an text classification tutorial with SageMaker, Hugging Face, and W&B.