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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:  Perform feature engineering on various use cases with minimal data movement  Rapidly develop and refine model features for data at scale with preferred tools  Seamlessly amplify existing investments in AI/ML tools   Apply strong governance, automation, and control of machine learning products   Ensure future-proof AI capabilities with Teradata’s open and connected ecosystem   Use open-source, partner, and native functions to train predictive models that can be used inside or outside of Vantage  Score and evaluate models at scale, then prepare for easy operationalization  Speaker: Alexander Kolovos 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.

Cost governance is a crucial part of data analytics, as it can affect the performance and profitability of an organization. Data analytics requires a lot of resources and can generate high costs. When these costs aren’t carefully monitored and optimized, organizations tend to overspend or underutilize their resources. To avoid these pitfalls, organizations need tools that help them measure and manage their data analytics spending. VantageCloud Lake provides the user-friendly financial governance features organizations need to manage their cloud data resources efficiently and cost-effectively. From the self-service Console, users can monitor usage in near real time, estimate future consumption, set alerts based on thresholds, and more. This enhances decision-making, enabling users to allocate resources to the right entities, prevent overruns, and eliminate inefficiencies. In this live demo, we’ll demonstrate VantageCloud Lake’s financial governance features and show you how to use the Console to gain greater visibility into costs—and centralized control over the financial governance of the entire organization. Join this live demo to see how to: Easily visualize current consumption and future estimates by department with drillable reports Set automated alerts based on consumption thresholds for policy-driven guardrails Estimate usage and cost for new environments in real time with interactive selections Create new profiles to estimate consumption with auto-scaling and cool-down options Use ask.ai, Teradata’s generative AI chat feature, to get pricing models, unit burn-down, and organization information Presenter(s) Seetha Bhat Director of Product Management Seetha is at the forefront of Teradata’s generative AI initiative, ask.ai. Her expertise lies in leveraging technology to significantly enhance the cloud data analytics experience and fuel product growth and innovation. Naresh Peddinti Staff Product Manager Naresh is responsible for self-service capabilities in the VantageCloud Lake Console. His expertise lies in building core financial governance and observability capabilities that help enterprise customers monitor consumption and avoid cost overruns.

As organizations struggle to manage uncontrolled data growth, traditional approaches to analyzing data quality, identifying business value with predictive analytics, and even performing basic observability have become time prohibitive and resource intensive.  Teradata VantageCloud Lake and ClearScape Analytics™ provide native in-database functions to deliver rapid and scalable data insights for the largest datasets stored in data lakes, warehouses, or lakehouse architectures.   Whether you’re just getting started with data and analytics, or you’re an expert data practitioner, you can use the power of VantageCloud Lake and ClearScape Analytics to explore and innovate with your data at massive scale.  Join this live demo to see how to: Enable users of any skill level to make better business decisions through data-driven insights and guidance Perform feature engineering on various use cases with minimal data movement and inspection Simplify your workflow and reduce complexity with intuitive built-in tools Boost your agility and responsiveness by accessing data insights anytime, anywhere, and on any device Presenter: Kevin Sturgeon Director, Solution Engineering Kevin Sturgeon directs a global program for the development and delivery of live demonstration assets showcasing Teradata’s unique technological and business benefits, including solutions for cloud, data and systems architecture, machine learning/AI/advanced analytics, and business outcomes.