Lightning AI is our way of meeting these needs. Machine learning initiatives that would have once taken a team of dedicated engineers weeks and months to build can now be completed virtually in a matter of days.

Cognex cameraprice

In addition to rebranding and launching a product that our team’s been dreaming about for years, we’re also announcing a $40M Series B funding round, led by Coatue and Index Ventures. This funding will allow us to continue expanding the Lightning AI platform, and to further simplify the AI development lifecycle.

That’s why after two years of laser focus on solving the issue of fragmentation in machine learning development, we are introducing the Lightning AI platform. With Lightning AI, researchers in academia, machine learning engineers in industry, and everyone in between will be able to bring to life end-to-end ML systems in a matter of days, rather than the years of work that currently requires.

Cognex cameraprogramming

Advanced Optical Character Recognition (OCR) uses AI-based edge learning to read characters on challenges surfaces – including uneven, reflective or low-contrast.

Since its inception, PyTorch Lightning has been downloaded over 20 million (!) times. Our new brand, Lightning AI, is our way of honoring the community of developers who’ve used and trusted the things we’ve built over the last three years.

Cognex821

Detect visual discrepancies with optimised error detection, using deep learning technology. Use your workforce smarter while keeping your lines running smoothly.

Automate label checks, seal checks, part ID, classification, code reading, traceability, presence or absence detection and much more to suit any industry.

Cognexindustries

Catch small, subtle defects with deep learning-based error detection. Solve applications or classify parts with variation based on multiple defect types or user-defined features.

This evolution of our company’s identity echoes our expanding focus on unifying the entire artificial intelligence development lifecycle. We’re also excited to announce a $40M Series B funding round, led by Coatue with participation from Index Ventures.

As PyTorch Lightning and Grid grew and began to serve a constantly expanding number of users across the globe, we made a significant effort to both listen to and act on the feedback and needs of our community. Throughout this process, two things became immediately clear:

Cognexinsightcamera

Writing this post feels a lot like opening up a never ending supply of birthday presents, only instead of video games and toy cars we get to share really exciting news.

Cognextools

You don’t have to know anything about the internal combustion engine to drive to the grocery store; why should you have to know about Kubernetes, cloud infrastructure, distributed file systems and fault-tolerant training to simply bring your AI project to life? Current solutions hand you the disparate pieces of a working car and hope that you’ll be able to assemble them into something that you can use to take a drive.

Solve simple inspections, bespoke applications or complex logic sequences. ID, sort or classify parts by multiple characteristics or run multiple inspections with a single camera.

This smart camera comes with customisable hardware for specific application needs. With optional modular lights, lenses and covers, vision experts can build the solution you need to remove the guesswork from inspection and monitoring.

Cognexartificial intelligence

The Cognex In-Sight 2800 brings together deep learning technology and traditional automated vision inspection tools to solve challenging manufacturing or logistics applications.

Modular hardware allows operators to customise the In-Sight 2800 for any application – adapting to new lines, product updates or regulatory changes.

CognexDeep Learning

Copyright © 2016-2024 Bytronic.com. All rights reserved. Registered in England and Wales No. 03348154. VAT Reg GB695393384.

Built for factory automation, the In-Sight 2800 allows manufacturers to solve complex inspections, increase product quality and maximise productivity. It can automate a wide range of applications using modular hardware.

As companies in every industry began putting PyTorch Lightning into production, we realized that the biggest challenge holding back AI adoption at scale was fragmentation of the AI ecosystem. I first noticed the impact of the fragmented AI ecosystem back in 2019. Because AI adoption at scale was and still is so nascent, every few months we would discover a “missing” part of the machine learning stack. Things like feature stores and data versioning, for instance, appeared as key tools in 2019. The following year, in 2020, we discovered the need for experiment managers and the ability to use multiple hardware accelerators without code changes (features that Grid pioneered that same year).

You’ll also notice some notches missing from the letter ‘n’ in our logotype. That’s a nod to Grid, the platform we built to train machine learning models on the cloud with just a single line of code.

The vision I’ve been pursuing since my time at NYU has always been to build something like an operating system for artificial intelligence, that allowed all the disparate pieces of the AI ecosystem to work together. No extant solution like this exists, and it took the collective focused effort of our entire team years to conceptualize and build Lightning AI.

A big thank you to everyone who’s used Grid over the years and become a part of our expert developer community. These kinds of leapfrogs in AI development are only possible because of the incredible work you all dao.

Why this matters is that every missing piece of the puzzle slows down the overall pace of AI innovation. An incomplete and fragmented stack makes it more time consuming and costly than it needs to be to build AI. Just getting a model to the point where it can be pushed into production takes hundreds if not thousands of developer hours spent purely on infrastructure. Now, “MLOPs” and “training on the cloud” are being positioned as the biggest problems facing AI adoption – problems we already solved in our previous product offerings. The real problem everyone in the AI space should be concerned with though is still the overarching problem of fragmentation.

Today, we’re introducing what we see as the first automobile of artificial intelligence. You focus on going for a drive – we’ll take care of the engineering.

After I began developing the open-source deep learning framework PyTorch Lightning as an undergrad at Columbia in 2015, I founded Lightning AI (which was Grid.ai at the time) in 2019 with Luis Capelo. While working on my PhD at NYU and Facebook AI Research, I open-sourced PyTorch Lightning and it quickly gained traction as the tool of choice for AI researchers and ML engineers in industry working on state-of-the-art artificial intelligence projects.