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Your New Cloud for AI May Be Inside a Colo

CIO Business Intelligence

Enterprises moving their artificial intelligence projects into full scale development are discovering escalating costs based on initial infrastructure choices. Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex.

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DS Smith sets a single-cloud agenda for sustainability

CIO Business Intelligence

The migration, still in its early stages, is being designed to benefit from the learned efficiencies, proven sustainability strategies, and advances in data and analytics on the AWS platform over the past decade. This enables the company to extract additional value from the data through real-time availability and contextualization.

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Belcorp reimagines R&D with AI

CIO Business Intelligence

Belcorp operates under a direct sales model in 14 countries. As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

For instance, for a variety of reasons, in the short term, CDAOS are challenged with quantifying the benefits of analytics’ investments. Some of the work is very foundational, such as building an enterprise data lake and migrating it to the cloud, which enables other more direct value-added activities such as self-service.

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P&G turns to AI to create digital manufacturing of the future

CIO Business Intelligence

The partners say they will create the future of digital manufacturing by leveraging the industrial internet of things (IIoT), digital twin , data, and AI to bring products to consumers faster and increase customer satisfaction, all while improving productivity and reducing costs. Data and AI as digital fundamentals.

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Accelerate data science feature engineering on transactional data lakes using Amazon Athena with Apache Iceberg

AWS Big Data

It manages large collections of files as tables, and it supports modern analytical data lake operations such as record-level insert, update, delete, and time travel queries. Data labeling is required for various use cases, including forecasting, computer vision, natural language processing, and speech recognition.

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Make Better AI Infrastructure Decisions: Why Hybrid Cloud is a Solid Fit

CIO Business Intelligence

Because it’s common for enterprise software development to leverage cloud environments, many IT groups assume that this infrastructure approach will succeed as well for AI model training. For many nascent AI projects in the prototyping and experimentation phase, the cloud works just fine.