Teradata today announced at Possible 2024: London new features and productivity enhancements to ClearScape Analytics, the most powerful, open, and connected AI/ML capabilities in the market today. These new features are designed to enable the world’s most innovative organizations to maximize the ROI of their AI/ML investments and boost data science productivity to achieve business outcomes faster and more efficiently.
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In recent years, the increased complexity of AI tools and platforms coupled with the proliferation of data and analytic platforms has resulted in complicated and inefficient AI/ML processes. As a result, companies are unable to derive complete insights from their data and the cost of AI operationalization at scale has risen. At the same time, data scientists are under growing pressure from their organizations to maximize productivity and increase their AI output. Unfortunately, due to inefficiency in data preparation, manual machine-learning processes, and the overarching challenges of AI operationalization, data science productivity is often hampered. This is then exacerbated by the steep learning curve that accompanies the industry’s rapidly evolving tools and techniques.
With ClearScape Analytics’ enhanced features and functionality, Teradata is addressing these challenges and enabling its customers to realize their full AI potential. All Teradata VantageCloud customers have access to ClearScape Analytics and these updates.
New ClearScape Analytics Features & Functionality
- Spark to ClearScape Analytics: Leverage Teradata’s tool, pyspark2teradataml, to easily convert legacy pyspark code to Teradata machine learning, eliminating the need for data movement. Benefits include:
- Reduce complexity and costs: Customers who previously needed to export data from VantageCloud to Spark platforms will no longer need the costly and cumbersome task. They can work with converted code in ClearScape Analytics.Operationalizing AI at scale: After conversion, customers can leverage VantageCloud’s enterprise-grade workload management, security, and data integration that is designed to operationalize trusted AI at scale and quickly get AI/ML models into production.Enabling multi-cloud machine learning: Customers can work in a true hybrid-cloud environment after conversion so they can get the most of their Spark-based investment.
- AutoML: Designed to enable data scientists to automatically train high-quality models specific to the business needs of each organization. Benefits include:
- Time-savings and expanded user base: By automating model training, Teradata is taking the time-consuming manual work involved in the ML process out of the equation and enabling non-technical business users to build AI/ML models.
- KNIME Integration: KNIME, a complete no-code, low-code platform that allows users to build data science workflows, is integrated with Teradata VantageCloud and ClearScape Analytics. Benefits include:
- Acceleration of AI initiatives and expanded user base: ClearScape Analytics users are provided with a free, open-source no-code interface that is designed to be suitable for a variety of technical and non-technical users. AI initiatives are expected to be accelerated with the simplicity of KNIME and scalability of VantageCloud.
- New self-service UX enhancements: New widgets enable a self-service user experience to access a variety of queries and plotting. Benefits include:
- Ease of use & self-service capabilities that is designed to reduce errors: Users can access their data with no coding, thereby reducing the risk of bad code or coding errors.
- Teradata Open-source ML: ClearScape Analytics users can run popular open-source machine learning functions on VantageCloud. Benefits include:
- Ease of use and scalability of open source: Ease of use of open-source functions on VantageCloud, scalability and performance for open-source functions, and operationalization of trained open-source models that are stored in VantageCloud.
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“We launched ClearScape Analytics nearly two years ago to help our customers maximize the value of their data, unlock innovation, and navigate AI complexity,” said Daniel Spurling, Senior Vice President, Product Management at Teradata. “With these latest enhancements, we’re helping data scientists streamline complex processes through various self-service and automated features that are designed to allow AI models to get from training to production to enterprise-wide operationalization at scale, faster and more cost effectively.”
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