Make ML models reproducible and safe.
Sizeless is a platform to help ML teams automatically run, test and improve their models to make ML reproducible and safe. Currently, ML engineers do this manually which takes weeks - we automate this down to a few clicks. ML engineers at Google shopping and computer vision startups are using our platform to shorten their ML development cycle and continuously monitor and fine tune their models. We provide ML engineers with a productivity tool set, so that they can focus on building great ML models. We developed this tool during our PhDs (Cornelius in ML at ETH Zurich and Roger in astrophysics at Cambridge), saving us and our PhD colleagues around 2 weeks per model while generating actionable insights. We launched 10 days ago, have 5 daily active users like ML engineering teams at Google Shopping and in computer vision startups and more than 50 sign ups on our waitlist. We provide ML engineers with a productivity tool set, so that they can focus on building great ML models.
Active Founders
Company Launches
TL;DR: sizeless makes it easy for ML practitioners to get their models from prototyping to production in days. Previously, highly trained ML engineers spent weeks trying to manually run, test and improve their models. With sizeless, engineers can focus on building great models - we take care of the rest and make sure that your models are safe and robust, all of this in just a few clicks.
Our Team 💪
We are Cornelius and Roger and want to make machine learning easy and safe. Machine learning is great but extremely hard to use, let alone reproduce! We are super excited to announce sizeless - a platform to automatically scale, benchmark and improve your ML models.
The Problem ❌
Machine learning is very fast-paced. Keeping up with every state-of-the-art model, reproducing results, and benchmarking your model on different data sets is a tedious and manual process. This takes weeks - even for highly trained engineers.
We encountered this problem often during our PhDs: Cornelius in machine learning/robotics at ETH Zurich and Roger in astrophysics at Cambridge.
Our Solution ✅
We automatically run your ML models at scale and perform benchmarks. This allows for reproducibility, transparency, and safety across different models and datasets. We help you flag and understand failure modes to speed up your model development cycle.
Can I use it? 🧐
Yes, please do so by uploading your ML model and data set to run and benchmark your model. For now, we are focusing on computer vision but will soon expand to other fields, e.g. ML, reinforcement learning, LLMs, and many more! Tell us which one you would love to see and use on our platform!
Our mission 👀
We make machine learning research accessible, reproducible, and transparent for everyone! We want to help you focus on what you enjoy doing most - building great and robust ML models. sizeless is built by and for ML engineers and practitioners.
Asks 🙏: Intros to ML teams who run, test, and deploy ML models
Feedback: We’d love it if you could try it out and provide feedback (sizeless.co)!
Questions: Shoot us an email (info@sizeless.co) with feedback or questions.
Network: Know any ML engineers deploying ML models? Please connect us!