Weights & Biases' LLM Developer Platform

Building and training LLMs undoubtedly cost an arm and a leg. While a detailed financial account of the cost certainly differs depending on the scale of the model, plenty of costs would have to be spent on power consumed and on the team of people hired.

Engineers then have to tirelessly spend their time and energy building a model that not only works but brings value to communities, organizations, and companies that need LLMs. In a market filled with plenty of LLMs already, it is also crucial to build models with something new to offer.

Aware of the challenges in building and training LLMs, with a wish to ease the process, startup Weights and Biases, founded by Lukas Biewald, Chris Van Pelt, and Shawn Lewis, creates an AI developer platform with toolkits and features designed to streamline LLM-building, making it more manageable and efficient.

Great Expertise with Unmatched Means

Built by machine learning practitioners for machine learning practitioners, Biewald formed the idea of Weights & Biases’ platform following his internship at OpenAI, where, despite having the chance to meet researchers with advanced expertise in the subject, Biewald had to work with outdated and inefficient tools.

One of the major issues Biewald faced was a substandard system of record-keeping, which may sound insignificant in the face of other intricate steps and processes of building LLMs but, in reality, plays quite a consequential role.

The experimental nature of building LLMs, which requires engineers to visualize metrics and datasets, evaluate bugs and errors, and track details of their machine learning pipeline, means thousands of experiments have to be run in, possibly, a hundred different ways, in order to find the perfect run.

After having to log experimental data into “simple” spreadsheets and screenshots, with no way of switching back into previous pipeline iterations when necessary, Biewald, along with Pelt and Google alumnus Lewis, started building Weights & Biases’ platform and the tools that come with it.

Interoperable Tools for Better Operations and Performance

Categorized as a machine learning operations (MLOps) platform, Weights & Biases’ tools allow engineers to not only conduct better record-keeping but also essentially build models more easily with a simple 5-minute setup.

The platform automatically tracks every activity in one’s machine learning pipeline as well as any changes made in experiments, with every experiment reproducible. This means users are given the ability to reproduce and visualize any checkpoints of their experiments at any time if necessary. Redundant screenshots and disorganized spreadsheets can become entirely void from the model-building process due to this feature.

Built to be helpful and instinctive for engineering teams, the platform has a centralized location that allows users to evaluate and visualize live metrics, datasets, logs, and stats in tables and graphs to efficiently manage their production together in real time. Finding and fixing errors is also made easy as experiment results can be compared side-by-side for more accurate debugging. Evaluating model performance and managing machine learning workflows becomes manageable.

Though the platform focuses on assisting engineers and developers, there exist monitoring tools that can be accessed by customers, as well. The tools allow customers to review datasets in case of issues such as visible sensitive data, for instance, before production starts. The platform also keeps track of reports that stakeholders might take an interest in before finalization.

Designed to make the ability to build better models faster more widespread, Weights & Biases’ MLOps platform allows flexible deployment, integrated with over 19K machine learning libraries and open source repositories, some of them being Pytorch, HuggingFace, TensorFlow, and OpenAI models.

It integrates with several training and inference environments as well, including Sagemaker, Azure ML, Vertex AI, and NVIDIA DGX. When it comes to infrastructure, the platform is compatible with AWS, Google Cloud, Microsoft Azure, NVIDIA, and Kubernetes.

Trusted by cutting-edge machine learning teams from OpenAI, NVIDIA, and Cohere, Weights & Biases ensures its platform is friendly to use early on. The platform has intro notebooks, quickstart programs, and an integrations guide as well as an API reference guide that makes every feature’s usage clear to users.

Last August, Weights & Biases raised $50M in funding, now totaling $250M, from former GitHub CEO Nat Friedman and ex-Y Combinator partner Daniel Gross, along with Coatue, Insight Partners, Felicis, Bond, BloombergBeta, and Sapphire.

Weights & Biases’ platform, whether cloud-hosted or in private hosting, is available for free for personal projects, with unlimited experiments and tracked hours along with 100 GB of storage and artifacts tracking. Those wishing to utilize the platform for corporate use can purchase the platform’s Teams package for $50/month in addition to extra charges according to usage. Enterprises can contact the Weights & Biases sales team for more information.

Meme & AI-Generated Picture

Job Posting

• Weights & Biases - Senior Software Engineer, Machine Learning Workflows - San Francisco, CA (Remote)

• Weights & Biases - Senior Software Engineer, Product - San Francisco, CA (Remote)

• Weights & Biases - Site Reliability Engineer - San Francisco, CA (Remote)

• Weights & Biases - Software Engineer, Platform Tooling - San Francisco, CA (Remote)

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