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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

Experiments

Experiment
tracking

Reports

Collaborative
dashboards

Artifacts

Dataset and model versioning

Tables

Interactive data visualization

Sweeps

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

wandb.init(project=’gpt3′)

# 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.