Weights & Biases (2024)

FAQs

Weights & Biases? ›

Build better models faster. Quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, and manage your ML workflows end-to-end.

Build better models faster. Quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, and manage your ML workflows end-to-end.

Weights control the strength of connections between neurons and capture relationships between input features and target outputs. Biases introduce adaptability and flexibility, allowing neurons to activate in response to various input conditions.

Weights & Biases, founded by Lukas Biewald, Chris Van Pelt and Shawn Lewis, helps machine learning engineers to track their data, automate their work and create collaborative dashboards for their teams. Customers include OpenAI, Lyft and ToyotaGitHub.

Weights and Biases vs Aim

Weights and Biases is a hosted closed-source MLOps platform. Aim is self-hosted, free and open-source experiment tracking tool.

Weights & Biases has 3 pricing editions. Look at different pricing editions below and see what edition and features meet your budget and needs. Weights & Biases is free for personal projects.

Examples of Weight Bias in the Media:

Using “thin” people as the beauty standard and encouraging others to have the same body type. Referring to someone as “obese” instead of saying “a person with obesity” Portraying people who are not considered thin as being “gross” or “undesirable”

Etymology. The word appears to derive from Old Provençal into Old French biais, "sideways, askance, against the grain". Whence comes French biais, "a slant, a slope, an oblique". It seems to have entered English via the game of bowls, where it referred to balls made with a greater weight on one side.

Be mindful and sensitive about the words to refer to someone's weight. Modeling respectful language is a way to show others how to do the same. Speak up if you witness someone engaging in weight-based teasing. “Fat jokes” are not funny – they are harmful and reinforce society's stigma.

Who owns Weights & biases? ›

Weights & Biases (W&B) was founded in 2017 by Lukas Biewald and Chris Van Pelt.

We use industry-standard security protocols to ensure that only you are able to access the data you send to us.

Lukas Biewald - Weights & Biases | LinkedIn.

Customers & case studies. Trusted by 800,000 users and 1000+ companies from the most cutting-edge and innovative AI startups and research institutions to the biggest brands around the world. Weights & Biases is the tool of choice for machine learning practitioners.

Weights & Biases is now valued at $1.25 billion, or $250 million more than after its previous funding round in late 2021. Since that funding round, the startup's installed base has ballooned from 100,000 users to 700,000.

Case Studies: MLflow and Weights & Biases in Action

MLflow, known for its lifecycle management capabilities, excels in areas such as experiment tracking, model packaging, and deployment. Weights & Biases, on the other hand, offers real-time insights with interactive dashboards and advanced experiment tracking.

It teaches attendees to manage their biases, change their behavior, and track their progress. It gives them information that contradicts stereotypes and allows them to connect with people whose experiences are different from theirs.

Bias allows you to shift the activation function by adding a constant (i.e. the given bias) to the input. Bias in Neural Networks can be thought of as analogous to the role of a constant in a linear function, whereby the line is effectively transposed by the constant value.

Bias is a disproportionate weight in favor of or against an idea or thing, usually in a way that is inaccurate, closed-minded, prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individual, a group, or a belief.

In other words, backpropagation aims to minimize the cost function by adjusting network's weights and biases. The level of adjustment is determined by the gradients of the cost function with respect to those parameters.