Bringing Applications to the Data: How to Use Cloud Data Platforms to Boost AI/ML Performance, Productivity, and Governance (Mexico Time)
Webinar Speaker: David Stodder, Senior Director of Research for BI, TDWI
Date: Monday, October 30, 2023
Time: 10:00 AM CST
Cutting-edge, data-rich applications are central to successful business innovation, improved customer engagement, supply chain agility, fraud detection, battling security threats, and much more. Developers infuse modern applications with AI/ML to increase speed, intelligence, and scale, especially for automated, real-time decision-making. Data management and data pipelines are critical to fueling AI/ML and enabling continuous model development, deployment, and testing.
The problem is that traditional architectures that bring data to applications cannot keep up with modern demands. This mode reduces the value and flexibility of development tools and environments such as Jupyter Notebooks, APIs, and containerization. Data burdens force developers to spend most of their time building and rebuilding data pipelines, increasing data latency. Organizations need a new approach for easier and faster development, feedback, and testing cycles that also increases reuse and repeatability.
Bringing applications to the data is a solution. By building applications natively on top of a cloud data platform, you can leverage high-performance computational processing capabilities directly where the data lives. Development teams can more easily test models and code. They can avoid data pipeline revision cycles, maximize the capabilities of development environments, and streamline deployment.
Join this TDWI Webinar to learn how you can overcome challenges and accelerate development and deployment of data-rich, AI/ML-infused applications on top of a high-performance cloud data platform.
Expert speakers will discuss:
- Developing and deploying AI/ML-infused applications natively on top of a cloud data platform
- Reducing data barriers to developing and deploying interactive AI/ML
- How to avoid ML data pipeline and model production surprises during deployment while also increasing agility
- Maximizing the value of Jupyter Notebooks, containerization, and APIs through integration with the data platform
- Addressing data governance and model governance requirements more effectively
Senior Product Marketing Manager
Maeve is a product manager turned product marketer focused on all things applications. Starting her career in software asset management and implementation, she shifted her focus to data and analytics challenges impacting businesses. Maeve holds an undergraduate degree in Information Technology Management from the University of Notre Dame. When her head is not in the data cloud, Maeve can be found skiing or skating through Golden Gate park.
Principal Architect, Data Apps, Field CTO
Brian has been in the data and analytics industry for over 20 years. He started his career doing applied research in mathematics and computer science at the National Security Agency. Since then he has had various technical and product roles in government and companies specializing in data warehousing, NoSQL, and analytics. Brian is currently Field CTO focusing on data applications.