Tensorleap

Your AI quality inspection dashboard is green. Your line is shipping. And somewhere, defects are slipping through. That's the silent failure problem in AI-based visual QC and it's structural. Without real-time ground truth, drift builds undetected. Lighting shifts after maintenance. Lenses haze. A new supplier lot changes surface texture. The model's confidence score ticks down, but nobody knows what that means at 2am on a production line. When an escape finally surfaces - a customer complaint, a yield excursion, an audit - the first question is always: "Since when?" If you can't answer that, you default to broad containment. Widen the quarantine. Re-inspect everything. Add manual review. Trust in automation erodes. The fix isn't more data or more QA layers. It's treating your CV model like a diagnosable production system with explainability as a control loop, not a report. When something breaks, teams need to pinpoint what changed and when, so they can make the smallest corrective fix instead of a full reset. We wrote about this in depth, including real factory failure patterns and how to contain them surgically: https://lnkd.in/dnV_TJKS

One of the biggest hidden costs in AI right now is the time spent training from scratch models that already exist elsewhere. In our latest webinar, we explored why traditional "search and tag" methods fail for large-scale model repositories. We demonstrated how Model Atlas uses weight-space transformations to reveal how models cluster and evolve. Why this matters for teams: Speed: Spot emerging trends in CV model design instantly. Efficiency: Identify high-performing fine-tunes for specific tasks. Insight: Trace lineage to understand how a model will actually behave in production. Watch the recording to see how we’re turning model sprawl into a navigable map: https://lnkd.in/dDAHdWkJ #MLOps #DeepLearning #ArtificialIntelligence #ModelObservability

One of the biggest hidden costs in AI right now is the time spent training models that already exist elsewhere. In our latest webinar, we explored why traditional "search and tag" methods fail for large-scale model repositories. We demonstrated how Model Atlas uses weight-space transformations to reveal how models cluster and evolve. Why this matters for teams: * Speed: Spot emerging trends in CV model design instantly. * Efficiency: Identify high-performing fine-tunes for specific tasks. * Insight: Trace lineage to understand how a model will actually behave in production. Watch the recording to see how we’re turning model sprawl into a navigable map: https://lnkd.in/d29N7seM #MLOps #DeepLearning #ArtificialIntelligence #ModelObservability

Happening today 👇 We’ll be discussing Model Atlas and how analyzing millions of AI models can reveal hidden patterns and relationships. Sign up here to receive the link: https://lnkd.in/eSFGfktA

Don’t miss our session on Model Atlas. Public model hubs now contain millions of AI models but understanding how they relate, evolve, and can be reused is still incredibly difficult. What if we could map the entire model ecosystem instead of browsing it one model at a time? We’ll explore Model Atlas, a new approach that analyzes weight-space relationships to reveal clusters, lineage, and hidden connections between models. 🎙 Featuring Eliyahu Horwitz (creator of Model Atlas) and Yotam Azriel (CEO, Tensorleap). Thu, Mar 12 11:00 AM EST | 5:00 PM CET 👉 Register here: https://lnkd.in/eSFGfktA #AI #DeepLearning #MachineLearning #ComputerVision #MLOps

There are now millions of public AI models. But there’s still almost no way to navigate them. In our upcoming webinar we introduced Model Atlas - a graph-based approach that maps model ecosystems through weight-space relationships. Hosted by Yotam Azriel (CEO, Tensorleap) with Eliyahu Horwitz (creator of Model Atlas). 📅 Thu, Mar 12 11:00 AM EST | 5:00 PM CET 👉 Receive the link upon registration: https://lnkd.in/eSFGfktA #AI #ComputerVision #DeepLearning #MLOps

We now have millions of public models. But there is almost no way to navigate them. We’re going live to introduce Model Atlas, a graph-based way to map model ecosystems using weight-space relationships. If you work with open models or fine-tuning pipelines, this will be a technical, practical session. Hosted by Yotam Azriel (CEO, Tensorleap) with Eliyahu Horwitz (creator of Model Atlas). 📅 Thu, Mar 12 11:00 AM EST | 5:00 PM CET 👉 Receive the link upon registration: https://lnkd.in/eSFGfktA

🗺️ What if you could navigate a million models like a map? Public model hubs have exploded in scale with no clear way to understand how they relate, where they came from, or which one is right for your use case. Eliahu Horwitz built Model Atlas to change that- turning large-scale model repositories into an interactive graph that connects models by attributes, fine-tuning relationships, and weight-space lineage. On March 12 2026 Eliahu joins our CEO Yotam Azriel for a live walkthrough grounded in real computer vision examples. 👉 Hit Attend and save your spot

⏰ Happening today, Jan 29 11:00 AM EST | 5:00 PM CET If you’re working with deep learning models in production, this session is about the failures you don’t see in your dashboards. Today’s webinar breaks down how to identify critical failure modes in neural networks- the specific data slices where models underperform and standard metrics fall short. We’ll cover practical, systematic approaches to uncovering these blind spots before they turn into production issues. Led by Amit Cohen Tom Koren alongside Yotam Azriel 👉 Register before we go live: https://hubs.ly/Q03ZrrF00

Coming Thursday, Jan 29th, we’re going live to show how to identify the failure modes hiding inside your data the ones standard evaluation never surfaces. 11:00 AM EST | 5:00 PM CET 👉 Register before we go live: https://hubs.ly/Q03ZrrF00