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Deliver AI agents with confidence on Amazon Bedrock with W&B Weave

Strategies, ops, tooling, and best practices

AI agents can enhance productivity, efficiency, and decision-making—but only if you can securely deploy them to production.

Download this whitepaper to learn:

  • How AI agents differ from traditional AI, and why it matters for your business
  • The 8 biggest deployment challenges and how to overcome them
  • A practical, step-by-step guide to developing AI agents using W&B Weave on Amazon Bedrock

Discover how to leverage Amazon Bedrock and W&B Weave to build, evaluate, monitor, and scale your AI agents, accelerating progress toward your team’s goals.

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Square accelerates the development and evaluation of new LLM candidates to power the Square Assistant, bringing conversational AI to businesses of all sizes.

Read the case study

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Canva optimizes MLOps using Weights & Biases, leveraging the Model Registry to seamlessly transition from experimentation to deployment. This empowers Canva’s ML team to enhance user experiences for over 150 million monthly active users through advanced AI capabilities in design and publishing.

Explore the success story

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Leonardo.ai leverages AWS and Weights & Biases to scale their GenAI platform, enabling creators to produce high-quality, customizable art assets for various industries. This collaboration accelerates the development and deployment of cutting-edge AI models, democratizing access to advanced GenAI tools.

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Scalable and Secure

We offer solutions that scale up with massive distributed training, and can be hosted in our secure hosted cloud or on your own private cloud in a self-hosted deployment.

With Weights & Biases you can:

Focus critical developer resources on your core business
Launch new machine learning models faster, with less back and forth
Safeguard IP with a central system of record
Onboard new ML engineers fast, and avoid duplicated work
READ CASE STUDY

A Case Study with TRI

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Overview

Toyota Research Institute’s mission is to build the safest mobility in the world. Machine learning teams at TRI are pursuing autonomous driving, and they use the Weights & Biases system of record to make their models reproducible.
  • Company size: 300+
  • Industry: Autonomous vehicles
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Problem

Led by Adrien Gaidon, the ML team built up world-class infrastructure for training models, but lacked a good way to track and version the valuable results.
They quickly realized the need for a central system of record, but building a solution internally was a distraction from the team’s core goals.
“It’s really hard for machine learning right now to provide any guarantees, statistical or otherwise, on how reliable it’s going to be. Putting in a safety critical system, it really has to work. How can we make it safe enough so that we can put it in cars and save lives instead of endanger lives.”
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Adrien Gaidon
Toyota Research Institute

Solution

The TRI team compared different solutions for their experiment tracking problem, and settled on Weights & Biases as the best platform to coordinate machine learning projects.

Instead of tinkering with brittle internal tools and ad-hoc solutions for experiment tracking and prediction visualizations, the ML team was able to standardize with Weights & Biases’ lightweight experiment tracking and visualization solutions.

The Weights & Biases dashboard gave machine learning practitioners a command center to compare across dataset and model versions, maintaining a reliable record of every experiment and result. ML engineers are now free to focus on the valuable work of model development, accelerating project progress.

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“You have to define the metrics clearly when you have a robotic system or a self-driving car that is extremely hard to test on the public roads for instance because the safety standards are very high, but at the same time you want continuous deployment and you want rapid iteration.”
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Adrien Gaidon
Toyota Research Institute

Join the top innovators around the world using Weights & Biases

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