How a centralized system of record accelerates experiment velocity
How W&B can help improve ML velocity on your team
Created on September 23|Last edited on September 23
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Without an MLOps platform, ML engineers spend too much time manually tracking experiments and analyzing results in spreadsheets and open source tools which don’t scale well. As a result, model development slows down, collaboration among team members is difficult, and ML team leaders lack visibility into their projects.
W&B Models makes your teams productive with a platform to iterate on models, analyze experiments, and collaborate cross seamlessly. It eliminates the need for disparate tools and manual handoffs between stages, minimizing errors and accelerating time-to-market.
If you'd like to get a holistic view about how it works, we've got a free whitepaper called 3 proven strategies to fast track AI projects that's a great place to start. You can download it via the button below:
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The best place to learn about how people use Weights & Biases is from people who actually use Weights & Biases. We thought we'd highlight two use cases that really resonate with our team: one from John Deere that reduces pesticides and makes farming a little bit easier and another from Northwest Medicine that helps their doctors improve the quality of patient care.
Customer spotlight: John Deere
Blue River Technology, owned by John Deere is building the next generation of smart machines that farmers use to control weeds and reduce costs in a way that promotes agricultural sustainability. They train computer vision models on PyTorch that identify which plants are weeds and which are crops so tractors can accurately spray pesticides on unwanted plants and not blanket their crops with it.
At Blue River Technology, performance is their top priority—building the fastest, most accurate models for deployment in the field. Working alongside farmers brings new problems every day and the need to constantly update models. PyTorch with W&B Models accelerates the iteration process and provides full visibility throughout, ensuring they can pinpoint the best-performing models for real-world application.
Customer spotlight: Northwest Medicine
Northwestern Medicine is improving patient outcomes with LLMs they train and manage on W&B. For example: when a patient comes in for a check-up, doctors routinely write up their notes about what follow-up procedures should happen and when. But hospitals can be chaotic and patient findings and recommendations can slip through the cracks. Literature suggests only about two-thirds of these recommendations get addressed.
The team at Northwestern built a model to address just this. Since rolling out their solution, they’ve helped thousands of patients get the right medical care at the right times. It’s not an exaggeration to say this is an LLM that saves lives.
What ties them together
On it's face, these are two very different use cases. But under the hood, both teams rely on unique, in-house data. Both teams are using ML to genuinely improve the world—less pesticides and better patient outcomes, to be specific. And both teams absolutely need to get it right.
Weights & Biases is used by thousands of the most innovative companies in the world because regardless of use case, W&B Models lets you track every experiment, brings powerful analytics, seamless collaboration, and deep visibility across the ML team and stakeholders across their organizations. Teams build better models faster on Weights & Biases.
You can hear more of their stories and dive into how our product helps teams like yours in the free guide below:
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