Remove category application-development
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Unlocking the Power of Containers: Exploring the Top 20 Docker Containers for Every Development Need

Analytics Vidhya

Introduction Docker containers have emerged as indispensable tools in the fast-evolving landscape of software development and deployment, providing a lightweight and efficient way to package, distribute, and run applications.

Software 265
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Nvidia AI Enterprise adds generative AI microservices

CIO Business Intelligence

of Nvidia’s enterprise-spanning AI software platform will feature a smorgasbord of microservices designed to speed app development and provide quick ways to ramp up deployments, the company announced today at its GPU Technology Conference. Version 5.0 Nvidia’s AI Enterprise 5.0 Containers, Generative AI, Microservices, Nvidia

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Delivering Low-latency Analytics Products for Business Success

Rocket-Powered Data Science

On-prem data sources have the powerful advantage (for design, development, and deployment of enterprise analytics applications) of low-latency data delivery. Low-latency data delivery is a system level requirement that is tied to a critical business user requirement: low-latency analytics product delivery !

Analytics 196
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The future of application delivery starts with modernization

IBM Big Data Hub

Where and how these applications are deployed will impact time to market and value realization. The reality is that application landscapes are complex, and they challenge enterprises to maintain and modernize existing infrastructure, while delivering new cloud-native features. How efficient is your release process?

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Simplify your query management with search templates in Amazon OpenSearch Service

AWS Big Data

This fully managed service allows organizations to secure data, perform keyword and semantic search, analyze logs, alert on anomalies, explore interactive log analytics, implement real-time application monitoring, and gain a more profound understanding of their information landscape. You don’t have to compile and redeploy your application.

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The InnoGraph Artificial Intelligence Taxonomy

Ontotext

As the first key step, we have built a comprehensive taxonomy of topics: AI technical topics and application areas (verticals). This post describes our approach to developing such a taxonomy by integrating and coreferencing data from numerous sources. Wikipedia categories are used to classify articles, and to form a hierarchy.

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AI Has an Uber Problem

O'Reilly on Data

They used their funding to do serious research and development, to build factories, cars, batteries, rockets and satellites. But even those settlements are likely to be bad for the development of a healthy entrepreneurial ecosystem. To be sure, sometimes companies do require a lot of capital to lay the groundwork for a possible future.

Marketing 153