Machine Learning Engineers: Put Perforated to the Test
Perforated is challenging a fundamental assumption in machine learning: that improving model performance means collecting more data or building bigger models. Its technology adds new learning signals during training to help models learn more efficiently, potentially achieving stronger performance with less training data and fewer parameters. It integrates directly into existing PyTorch workflows without requiring a complete model rebuild.
At TechFest 2026, we're putting that claim to the test.
We're inviting machine learning engineers, AI engineers, data scientists, and developers working with neural networks to participate in a live technology test. See the technology in action, examine the results, ask the hard technical questions, and bring your own perspective on whether the approach delivers.
NO PITCH-It's a chance to test the technology.
Come ready to challenge assumptions, scrutinize the results, and see what happens when Perforated's approach is put in front of people who actually build and optimize ML models.
If you work with PyTorch, model optimization, computer vision, neural networks, pruning, quantization, or data-efficient ML, we want you in the room.
