The Cutting-EDGE of MLOps

Building compliant, efficient and real-time Edge AI

Gain Insights from Leaders in Edge MLOPs

Maintaining Compliance & Data Privacy through Federated Learning
Roundtable. 6:00 - 6:25 pm

In this session, we will discuss the impact of regulation on AI development and the future landscape of AI and data privacy with leading organizations using Federated Learning technology.

We will learn the technical aspects of how Federated Learning is being applied in healthcare as well as in the industrial world.

How to handle zero latency AI in production at scale
Roundtable. 6:25 - 6:45 pm

Instant decision-making and processing is critical  in industries, such as autonomous vehicles, industrial automation, smart grids where the slightest delay can have significant consequences.

In this panel we will cover the infrastructure requirements for real-time AI and deployment tools and platforms that are optimized for edge AI.

Optimization for data-intensive deployments
Roundtable. 6:45 - 7:15 pm

Model compression techniques and the use of hardware accelerators, such as GPUs and specialized AI chips, pose new distributed computing scenarios for data-intensive applications.

In this round table, we will discuss scalability challenges for data-intensive deployments and the techniques used for optimizing Machine Learning Operations.

Reliable and secure ML to ensure business continuity
Roundtable. 7:15 pm - 7:35 pm

Deploying robust, and secure ML systems for uninterrupted business operations is key in industries such as energy, oil and gas, water utility and critical infrastructures where independence from connectivity, suppliers, and changing conditions is paramount to business continuity.

In this session, we will share best practices in safeguarding ML deployments in the Edge.

Meet our Panellists

VP Engineering
CEO and Co-founder
Co-founder & Head of Product
AI Solution Engineer
CTO and co-founder
Head of Developer Relations
Global Head of Developer Relations
CEO and co-founder
Host and CNCF Ambassador

Why attend?

  • Learn best practices in deploying AI in the Edge and how organizations are responding to the challenges it poses to MLOps infrastructures.
  • Discover the tools, systems and structures you need to put in place to be ready to scale in the Edge.
  • Stay ahead of the curve in Edge AI, which is according to Gartner, the most impactful technology since Cloud.

Who should attend?

  • AI and ML Teams looking into how to implement Machine Learning algorithms in the edge.
  • ML engineers seeking to master the Edge.
  • Data Scientist wishing to build efficient and accurate models for resource-constrained devices.
  • Technologists in the field of MLOps looking to break into Edge AI.