talk-data.com

Kubernetes has become the backbone of scalable, resilient infrastructure but deploying complex AI systems on it still raises questions around configuration, scaling, and monitoring. In this hands-on session, Eric Newby, our Head of Product, will walk through how to deploy ecosystem.Ai on Kubernetes, from setup to optimization.

We’ll explore how to containerize and orchestrate AI components, manage workloads efficiently, and ensure performance consistency across environments.

A joint webinar exploring how AI is transforming rewards platforms by enabling real-time personalization, micro-rewards, and behavior-based engagement to build trust and long-term value.

Join us as we explore causal accountability to improve customer engagement, using the advantages afforded by generative AI. Dr. Eric Newby, who brings his profound expertise in Applied Mathematics and a keen understanding of machine learning, will delve into an in-depth discussion on causal inference. He will shed light on how causality increases the predictability and effectiveness of machine learning models. The talk will explore the complexities of causality within data, offering invaluable insights into how to correctly interpret correlations and anomalies.

The understanding of these influences leads to better decision-making, improved algorithm performances, and eventually, more reliable and robust Machine Learning applications. Furthermore, Dr. Newby will guide you on how to implement fundamental changes in designing machine learning models keeping causality in mind. This includes overcoming challenges in identifying causal relationships and accurately modeling these in the machine learning context.

This event promises to be an engaging and enlightening experience for data scientists, machine learning enthusiasts, algorithm designers, software engineers, and all others interested in the interplay of causality in machine learning and wishing to improve the efficiency and effectiveness of their models and applications.

Join us as we explore causal inference and its significant role in creating effective machine learning algorithms.

Dr. Eric Newby, who brings his profound expertise in Applied Mathematics and a keen understanding of machine learning, will delve into an in-depth discussion on causal inference. He will shed light on how causality increases the predictability and effectiveness of machine learning models. The talk will explore the complexities of causality within data, offering invaluable insights into how to correctly interpret correlations and anomalies.

The understanding of these influences leads to better decision-making, improved algorithm performances, and eventually, more reliable and robust Machine Learning applications. Furthermore, Dr. Newby will guide you on how to implement fundamental changes in designing machine learning models keeping causality in mind. This includes overcoming challenges in identifying causal relationships and accurately modeling these in the machine-learning context.

This event promises to be an engaging and enlightening experience for data scientists, machine learning enthusiasts, algorithm designers, software engineers, and all others interested in the interplay of causality in machine learning and wishing to improve the efficiency and effectiveness of their models and applications. Enhance your knowledge, decode the impacts of causality, and learn how to implement change in machine-learning applications