Build performant, safe, and trustworthy AI at scale [OnDemand]
Whether you buy AI models from external vendors or build them internally, the key technical challenges remain the same – expensive to obtain datasets, huge class imbalance, data shifts, data leakage or model degradation after deployment.
So, how do you know what is causing your AI models to fail, and how do you stop that from happening?
Discover all the answers and more by catching the replay of our live session with LatticeFlow.
3 key takeaways:
- Buy or build: Regardless if you buy or build, understanding your AI model failures and risks is critical.
- The necessity of identifying hidden blind spots: Preemptively identifying hidden model blind spots before deployment is becoming essential.
- Understand your data at scale: To help you make informed decisions.
Meet your expert host...
Dr. Pavol Bielik
Co-Founder & CTO at LatticeFlow
Dr Pavol Bielik earned his PhD at ETH Zurich, specializing in machine learning, symbolic AI,
synthesis, and programming languages. His groundbreaking research earned him the
prestigious Facebook Fellowship in 2017, representing the sole European recipient, along with
the Romberg Grant in 2016.
Following his doctorate, Pavol's passion for ensuring the safety and reliability of deep learning
models led to the founding of LatticeFlow AI. Building on more than a decade of research, Pavol
and a dynamic team of researchers at LatticeFlow AI developed a platform that equips
companies with the tools to deliver robust and high-performance AI models, utilizing automatic
diagnosis and improvement of data and models.
