Virtual Fireside Chat & AMA with Amila Perera, VP of R&D & Sandy Kirk, Director of OTS (Off-the-Shelf) Training Datasets
OTS Advantage: Build Better Models Without the Data Delay
Join a virtual fireside chat + live AMA with world-class AI researchers Amila who's team is building OTS datasets to help frontier labs go to production faster and Sandy leading this initiative!
Training data is often the biggest bottleneck in AI development. Custom collection takes time, slows evaluation, and makes it harder to improve models quickly across new domains and harder tasks.
What You’ll Learn
Why custom data collection becomes a bottleneck for AI teams.
How production-ready datasets can speed up post-training and evaluation.
Where off-the-shelf datasets fit across STEM, coding, finance, healthcare, multimodal, robotics, and specialized domains.
How advanced difficulty tiers help uncover reasoning gaps and model weaknesses.
When semi-custom and targeted extensions make sense for evolving needs .
The Problem
Building high-quality AI systems requires more than raw data. Teams need structured, reliable, domain-specific datasets that are ready to use, scalable, and difficult enough to expose real model limitations .
Common challenges include:
Slow custom data collection and labeling cycles.
Limited coverage across specialized domains.
Difficulty finding advanced, benchmark-style questions and tasks.
Gaps in evaluation data for reasoning, workflows, and multimodal use cases.
Rework when requirements change after data collection has already started .
The Solution
Off-the-shelf datasets offer a faster path forward. Instead of waiting for a custom build, teams can access pre-built training and evaluation data designed for production use, with coverage across multiple domains and use cases .
Benefits include:
Faster time to training and evaluation.
Thousands of structured questions across core domains.
Graduate and PhD-level Q&A and QSA datasets.
Continuous refinement to help close reasoning gaps.
Adaptable datasets that can be extended for specific model requirements .
Dataset Areas Covered
Our webinar will highlight dataset coverage in:
STEM: mathematics, physics, biology, chemistry, and engineering.
Coding: generation, debugging, software reasoning, and benchmark-style evaluation.
Agentic workflows: multi-step reasoning, tool use, and structured tasks.
Multimodal: text, image, audio, and interface interactions.
Finance: applied research, deep research, and analytical reasoning.
Healthcare: medical case studies and medical Q&A.
Robotics and physical AI: embodied reasoning and real-world task execution.
Specialized domains: niche workflows, edge cases, CBRNE, and 3D CAD .
Who Should Attend
This webinar is a fit for:
AI and ML leaders building foundation or domain models.
Data teams responsible for post-training and evaluation.
Product teams evaluating model readiness.
Researchers looking for harder, more structured datasets.
Organizations that need domain expansion without long custom delays .
About the host:
👋 Ken Morimoto is a veteran startup & tech operator (ex-Amazon, Scale, NewEdge, Bigband, Cobalt), active angel/VC, community builder, Director at Innodata, Chair of AI Circle (Seattle), and GP at Leading Edge VC. He’s active in the Seattle, NYC, and Bay Area tech communities and has been actively hosting AI Leaders & Builders meetups since 2020.