Wednesday, April 17 2024 8:00 a.m. – 8:40 a.m. PST
Webinar
オンライン
Organizations using Databricks often struggle to build and operationalize AI/ML models at scale due to its immature workload management, lack of hybrid architecture, and inaccessibility to low-code/no-code users.
If you’re seeking a more flexible, scalable, and cost-effective solution, you can now use Teradata’s new conversion tool, pyspark2teradataml, to seamlessly migrate your PySpark workloads to an equivalent format that works in Teradata Vantage™ with minimal additional coding. With this approach, data scientists can leverage Teradata’s truly multi-cloud architecture to develop, train, and score models while limiting data movement from tools such as Databricks. This will enable organizations to operationalize AI/ML at scale with reliability while significantly reducing overall costs, complexity, and time-intensive work.
Join this live demo to learn more about this new module in Teradata’s Python library and see a step-by-step implementation of a workflow using a PySpark-based use case entirely on a Vantage system.
Learn how this new feature can help you:
Sr. Software Architect, Product, Teradata
Alexander Kolovos is a Senior Software Architect in Teradata’s Product Engineering group. His experience extends across the Languages Ecosystem products with key contributions to the R and Python integration for Teradata Vantage™ in-nodes scripting. He also authored the Teradata Orange Book titled “R And Python Analytics With the SCRIPT Table Operator.” He is the Product Owner for a range of ClearScape Analytics™ capabilities like Open Analytics Framework, third party integration, and language client libraries.