¡Llega DataArt IT NonStop 2021!
Del 18 al 20 de noviembre, expertos de empresas como NVIDIA, Microsoft, Ocado, DataArt, Codete y SoftServe, se reunirán en este evento global para hablar sobre Inteligencia Artificial, Machine Learning, Cloud Computing y Data Science. El evento será online, en inglés y es completamente gratuito.
Para participar, sólo hay que registrarse en https://it-nonstop.net/
Algunas de las conferencias que se llevarán a cabo durante DataArt IT NonStop 2021 serán:
- Harnessing the virtual realm for applied AI
Alison B Lowndes (NVIDIA)
Artificial Intelligence is impacting all areas of society, from healthcare and transportation to smart cities and energy. It is important to understand the interaction between gaming & extended reality, graphics, AI, robotics, simulation, high performance scientific computing, healthcare & more. You will be introduced to the current state of the art across academia, enterprise and startups.
- Avoid mistakes building AI products
Karol Przystalski (Kodete)
Based on Gartner's research, 85% of AI projects fail. In this talk, we show the most common mistakes made by managers, developers, and data scientists while building AI products. We go through ten case studies of products that failed and analyze the reasons for each failure. We also present how to avoid such mistakes and deliver a successful AI product by introducing a few lifecycle changes. This talk will be useful for product and project managers, decision-makers and Machine Learning Engineers.
- To trust or not to trust AI, that is the question
Hrant Davtyan (Pinsight)
The widespread usage of AI systems creates ethical and legal problems when the latter is built upon unfair datasets. Unfortunately, almost any data in the world has an inherent bias that AI systems "love" to capture and follow. It is of the utmost importance both from a legal and ethical perspective one ensures that AI systems that are created and deployed into production meet the fairness criteria and provide equal opportunities in decision-making. The talk will introduce famous examples of biased decision-making and ways/tools to capture and mitigate them. The talk will be centered around AI biases and sounds examples of unfair or wrong decisions made after using ML/AI. It will touch on topics related to AI fairness, responsibility, and explainability.
- Breaking Machine Learning Models
Ivaylo Strandjev (HyperScience)
Machine Learning has an ever-increasing role in everyone's life. As this happens, we need to be aware of and prepared for potential risks and issues that might arise. In this session, we will focus on several approaches that can be used to "break" machine learning to trick it into making wrong or manipulated predictions, and what can be done to address them. These problems are shared between practically all ML models which makes them universal and particularly important.
- Machine Learning - Build, Train, Deploy. And what next? MLOps to the rescue!
Konrad Lyda (DataArt)
Machine Learning is a powerful tool in the arsenal of enterprises looking to gain a competitive edge in today's business climate. At some point in the lifecycle of our Machine Learning model building, we need to get out of the warm environment of our laptop and deploy our solutions at a larger scale. However, MLOps tools that help you create and implement these systems can be difficult to identify, let alone implement correctly. This guide cuts through the noise, highlighting important MLOps considerations that you must consider when creating and implementing your next machine learning-based system.
La agenda completa se puede ver en: https://it-nonstop.net/