테라다데이터 정보
테라데이터는 기업들이 지능을 자율적 행동으로 전환할 수 있도록 지원하며, AI 에이전트가 깊은 비즈니스 맥락과 신뢰할 수 있는 데이터에 기반을 두도록 합니다. AI 에이전트가 늘어나면서, 테라데이터는 기업들이 지금 필요로 하는 컨텍스트 엔진, 거버넌스 계층, 그리고 성능 기반이 되었습니다. Teradata 자율 지식 플랫폼은 클라우드, 온프레미스, 하이브리드 환경에서 AI를 생산에 투입합니다.
Teradata Brings Elastic Python Compute to AI Studio, Unifying Data, Governance, and AI at Enterprise Scale
New distributed Python capability accelerates large-scale AI operations while keeping data and compute within the Teradata platform
Teradata (NYSE: TDC) today announced the general availability of Elastic Python Compute within AI Studio. The capability gives enterprise data science teams a managed, distributed Python environment for scaling AI compute and machine learning workloads through parallel Python execution for data engineering, machine learning, and model development.
Elastic Python Compute is built on Ray, a distributed computing framework, and integrates natively with the Teradata Autonomous Knowledge Platform and AI Studio. Data scientists can parallelize model training, execute custom Python applications at scale, and serve high-scale ML and AI workloads without the overhead of manual cluster setup or managing Ray clusters. Elastic Python Compute supports both single-server and cluster deployments, giving organizations the flexibility to scale from individual workloads to enterprise-scale operations, with faster development cycles, stronger governance controls, and lower infrastructure costs.
Keeping Data and AI Compute Within the Platform
Elastic Python Compute supports local model hosting and governance, with no external API dependencies and no token-based inference costs. AI workloads run entirely within the Teradata platform, including AI Studio, with no data movement required. Elastic Python Compute reads and writes data directly to the Teradata Database, S3 object storage, and Open Table Format catalogs, such as Apache Iceberg and Delta Lake, enabling data science teams to incorporate files and images for model training and scoring. Flexible cluster management, including the ability to run separate clusters for different workloads, allows teams to optimize cost and performance by selecting CPU or GPU compute based on the task at hand. Massive parallel processing supports large-scale AI and data workloads across cloud, on-premises, and hybrid deployments.
The platform also supports Arrow Flight SQL, enabling parallel, columnar data transfer between applications and the Teradata platform at speeds up to 84x faster than traditional row-based transfer methods, based on Teradata benchmark testing. *
Customer Impact
A retail bank had been exporting tens of millions of customer records to local Python environments for credit feature engineering, a practice that created data governance gaps and put the organization at risk of breaching data residency regulations. With AI Studio's in-database analytics, credit scoring pipelines covering payment history, utilization ratios, and delinquency flags now run three times faster while remaining within the governance perimeter. A complete audit trail from raw data through feature engineering to final model output allows regulators to replay any step in the process on demand.
For a major telecommunications provider, the challenge was scale. Its data science team had been exporting billions of call detail records and network telemetry events to external Python clusters, a slow, costly process that created data sovereignty risk. Feature engineering across call drop rates, signal quality indices, and ARPU (average revenue per user) trends now runs directly inside the Teradata platform. The churn prediction model trains entirely within the provider's own infrastructure, satisfying both GDPR obligations and national data residency requirements. Feature engineering runtime fell from four hours to twenty minutes, and model accuracy improved by twelve percent as a result of training on the full dataset rather than a constrained sample.
In the public sector, a government maritime agency needed to track vessels switching off their transponders to obscure their movements. Using Elastic Python Compute, the agency's data science team combined satellite imagery, radar signals, AIS data, and commercial imaging to reconstruct vessel movements that would otherwise go undetected. AI-driven analytics fused these diverse data sources into actionable maritime intelligence, improving visibility and enabling faster, more effective responses to security threats at sea.
Executive Perspective
"Enterprise data science teams have spent years working around the fundamental constraint of moving data outside their platforms to build and train AI models, accepting the governance risk and cost that comes with it. Elastic Python Compute removes that constraint. Teams can now run distributed AI workloads at scale where their data already lives, and get to production faster without compromising on governance or cost control."
— Sumeet Arora, Chief Product Officer, Teradata
Availability
Elastic Python Compute is generally available within AI Studio on AWS and Microsoft Azure. Availability on Google Cloud is expected in Q4 2026, with on-premises available now with Teradata Factory.
* Performance results may vary. Benchmarks were conducted in a specific test environment and may not reflect results in your infrastructure.