Teradata (NYSE:TDC) announced novel features and productivity enhancements for ClearScape Analytics, the most powerful, open and connected AI/ML capability on the market. These novel features are designed to enable the world’s most groundbreaking organizations to maximize their AI/ML ROI and boost data science productivity to achieve business outcomes faster and more efficiently.
In recent years, the increased complexity of AI tools and platforms, combined with the proliferation of data and analytics platforms, has led to intricate and unproductive AI/ML processes. As a result, companies are unable to extract full insights from their data, and the cost of operationalizing AI at scale has increased. At the same time, data scientists are under increasing pressure from their organizations to maximize productivity and drive AI performance. Unfortunately, due to data preparation inefficiencies, manual machine learning processes, and the overarching challenges of operationalizing AI, data science productivity is often circumscribed. This is further exacerbated by the steep learning curve that comes with rapidly evolving tools and techniques in the industry.
With enhanced features and functionality in ClearScape Analytics, Teradata addresses these challenges and enables its customers to fully leverage the power of AI. All Teradata VantageCloud customers have access to ClearScape Analytics and these updates.
Recent Features and Functionality of ClearScape Analytics
- From Spark to ClearScape Analytics: Operate Teradata’s pyspark2teradataml tool to easily convert legacy pyspark code to Teradata machine learning, eliminating the need to move data. Benefits include:
- Reduce complexity and costs: Customers who previously had to export data from VantageCloud to Spark platforms will no longer need this steep and cumbersome task. They can work with the converted code in ClearScape Analytics.
- Deploying AI at Scale: After conversion, customers can leverage VantageCloud’s enterprise-class workload management, security, and data integration solutions designed to deploy trusted AI at scale and quickly deploy AI/ML models to production.
- Enabling machine learning across multiple clouds: After conversion, customers can operate in a true hybrid cloud environment, allowing them to fully leverage their Spark investment.
- AutoML: Designed to enable data scientists to automatically train high-quality models tailored to the business needs of any organization. Benefits include:
- Save time and expand your user base: By automating model training, Teradata eliminates the time-consuming manual work associated with the machine learning process and enables non-technical business users to build AI/ML models.
- KNIME Integration: KNIME, a complete no-code and low-code platform that enables users to build data science workflows, is integrated with Teradata VantageCloud and ClearScape Analytics. Benefits include:
- Accelerate AI initiatives and expand user base: ClearScape Analytics users get a free, open, no-code interface that is designed for a variety of technical and non-technical users. AI initiatives are expected to be accelerated by the simplicity of KNIME and the scalability of VantageCloud.
- Recent UX improvements in self-service mode: Recent widgets enable user self-service with various queries and charts. Benefits include:
- Ease of employ and self-service capabilities to reduce errors: Users can access their data without having to code, reducing the risk of bad code or coding errors.
- Teradata ML Open Source: ClearScape Analytics users can run popular open-source machine learning functions on VantageCloud. Benefits include:
- Open source ease of employ and scalability: The ease of using open source features in VantageCloud, the scalability and performance of open source features, and the operationalization of trained open source models stored in VantageCloud.