New ClearScape Analytics Features Maximize AI/ML Investments and Boost Data Science Productivity
Teradata (NYSE: TDC) has announced new features for its ClearScape Analytics platform at Possible 2024: London. These enhancements aim to maximize AI/ML investments and boost data science productivity for organizations. Key updates include:
1. Spark to ClearScape Analytics conversion tool to reduce complexity and costs
2. AutoML for automated model training
3. KNIME Integration for no-code/low-code data science workflows
4. New self-service UX enhancements for easier data access
5. Open-source ML support on VantageCloud
These features address challenges in AI/ML processes, aiming to streamline operations, reduce costs, and accelerate AI initiatives for both technical and non-technical users.
Teradata (NYSE: TDC) ha annunciato nuove funzionalità per la sua piattaforma ClearScape Analytics durante Possible 2024: Londra. Questi miglioramenti mirano a massimizzare gli investimenti in AI/ML e aumentare la produttività della scienza dei dati per le organizzazioni. Le principali novità includono:
1. Strumento di conversione Spark a ClearScape Analytics per ridurre complessità e costi
2. AutoML per l'allenamento automatizzato dei modelli
3. Integrazione KNIME per flussi di lavoro di data science senza codice/basso codice
4. Nuove migliorie UX self-service per un accesso più facile ai dati
5. Supporto per il ML open-source su VantageCloud
Queste funzionalità affrontano le sfide nei processi di AI/ML, mirando a semplificare le operazioni, ridurre i costi e accelerare le iniziative di AI per utenti tecnici e non tecnici.
Teradata (NYSE: TDC) ha anunciado nuevas características para su plataforma ClearScape Analytics en Possible 2024: Londres. Estas mejoras tienen como objetivo maximizar las inversiones en AI/ML y incrementar la productividad en ciencia de datos para las organizaciones. Las actualizaciones clave incluyen:
1. Herramienta de conversión Spark a ClearScape Analytics para reducir la complejidad y los costos
2. AutoML para el entrenamiento automatizado de modelos
3. Integración KNIME para flujos de trabajo de ciencia de datos sin código/bajo código
4. Nuevas mejoras en la UX de autoservicio para un acceso más fácil a los datos
5. Soporte para ML de código abierto en VantageCloud
Estas características abordan desafíos en los procesos de AI/ML, con el objetivo de optimizar las operaciones, reducir costos y acelerar las iniciativas de AI tanto para usuarios técnicos como no técnicos.
테라데이타(Teradata)(NYSE: TDC)가 Possible 2024: 런던에서 ClearScape Analytics 플랫폼의 새로운 기능을 발표했습니다. 이 개선 사항은 AI/ML 투자 극대화와 조직의 데이터 과학 생산성 향상을 목표로 하고 있습니다. 주요 업데이트는 다음과 같습니다:
1. 복잡성 및 비용을 줄이기 위한 Spark에서 ClearScape Analytics로의 변환 도구
2. 모델 학습 자동화를 위한 AutoML
3. 코드 없는/저코드 데이터 과학 워크플로우를 위한 KNIME 통합
4. 데이터 접근을 용이하게 하는 새로운 셀프 서비스 UX 개선
5. VantageCloud에서의 오픈 소스 ML 지원
이 기능들은 AI/ML 프로세스의 과제를 해결하고, 운영을 간소화하며, 비용을 절감하고, 기술 및 비기술 사용자 모두를 위한 AI 이니셔티브를 가속화하는 것을 목표로 합니다.
Teradata (NYSE: TDC) a annoncé de nouvelles fonctionnalités pour sa plateforme ClearScape Analytics lors de Possible 2024 : Londres. Ces améliorations visent à maximiser les investissements en IA/ML et à stimuler la productivité en science des données pour les organisations. Les mises à jour clés comprennent :
1. Outil de conversion Spark vers ClearScape Analytics pour réduire la complexité et les coûts
2. AutoML pour l'entraînement automatisé des modèles
3. Intégration KNIME pour des flux de travail en science des données sans code/bas code
4. Nouvelles améliorations de l'UX en libre-service pour un accès facilité aux données
5. Support pour le ML open-source sur VantageCloud
Ces fonctionnalités répondent aux défis des processus d'IA/ML, visant à simplifier les opérations, réduire les coûts et accélérer les initiatives d'IA tant pour les utilisateurs techniques que non techniques.
Teradata (NYSE: TDC) hat auf der Possible 2024: London neue Funktionen für die ClearScape Analytics-Plattform angekündigt. Diese Verbesserungen zielen darauf ab, AI/ML-Investitionen zu maximieren und die Datenwissenschaftsproduktivität für Organisationen zu steigern. Die wichtigsten Updates umfassen:
1. Umwandlungswerkzeug von Spark zu ClearScape Analytics, um Komplexität und Kosten zu reduzieren
2. AutoML für das automatisierte Training von Modellen
3. KNIME-Integration für No-Code/Low-Code-Datenwissenschafts-Workflows
4. Neue Self-Service UX-Verbesserungen für einen einfacheren Datenzugriff
5. Unterstützung von Open-Source-ML auf VantageCloud
Diese Funktionen adressieren Herausforderungen in AI/ML-Prozessen, mit dem Ziel, Abläufe zu rationalisieren, Kosten zu senken und AI-Initiativen sowohl für technische als auch für nicht-technische Nutzer zu beschleunigen.
- Introduction of Spark to ClearScape Analytics conversion tool, potentially reducing data movement costs
- AutoML feature for automated model training, expanding user base to non-technical business users
- KNIME integration providing no-code/low-code platform for data science workflows
- New self-service UX enhancements for easier data access without coding
- Support for open-source ML functions on VantageCloud, improving scalability and performance
- None.
Insights
Teradata's ClearScape Analytics update is a strategic move to address key challenges in AI/ML adoption. The new features aim to streamline data science workflows and improve ROI for AI investments. Particularly noteworthy is the Spark to ClearScape Analytics conversion tool, which could significantly reduce data movement costs and complexity.
The introduction of AutoML and KNIME integration demonstrates Teradata's focus on democratizing AI, potentially expanding the user base beyond traditional data scientists. This aligns with industry trends towards no-code/low-code solutions and could accelerate AI adoption in enterprises.
However, the real test will be in the practical implementation and performance gains these features deliver in real-world scenarios. Teradata's success will depend on how effectively these tools can simplify AI operationalization at scale, a persistent challenge in the industry.
The enhancements to ClearScape Analytics address several pain points in the data science workflow. The Spark to ClearScape Analytics conversion tool is particularly intriguing, as it could significantly reduce the need for data movement, a common bottleneck in ML pipelines.
The AutoML feature, while not unique in the market, could be a game-changer if it truly delivers high-quality models tailored to specific business needs. This, combined with the KNIME integration, has the potential to democratize ML, enabling a broader range of users to leverage AI capabilities.
However, it's important to note that while these tools can enhance productivity, they don't replace the need for skilled data scientists. The true value will lie in how well these features integrate into existing workflows and their ability to handle complex, real-world data scenarios.
From a financial perspective, Teradata's enhancements to ClearScape Analytics could potentially boost the company's competitive position in the rapidly growing AI/ML market. By addressing key pain points like data movement costs and AI operationalization, Teradata is positioning itself to capture a larger share of enterprise AI spending.
The focus on improving ROI for AI investments is particularly timely, given the current economic climate where companies are scrutinizing tech spending. If Teradata can demonstrate tangible cost savings and productivity gains, it could drive increased adoption of its VantageCloud platform.
However, investors should monitor customer adoption rates and revenue impact in the coming quarters to gauge the success of these enhancements. The AI/ML market is highly competitive and Teradata will need to show clear differentiation to drive meaningful growth in this segment.
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
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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.
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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.
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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.
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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.
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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.
"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.”
About Teradata
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See how at Teradata.com.
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MEDIA CONTACT
January Machold
January.Machold@Teradata.com
Source: Teradata
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