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Revolutionizing data management: Trends driving security, scalability, and governance in 2025

CIO

From data masking technologies that ensure unparalleled privacy to cloud-native innovations driving scalability, these trends highlight how enterprises can balance innovation with accountability. With machine learning, these processes can be refined over time and anomalies can be predicted before they arise.

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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production.

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How today’s enterprise architect juggles strategy, tech and innovation

CIO

tagging, component/application mapping, key metric collection) and tools incorporated to ensure data can be reported on sufficiently and efficiently without creating an industry in itself! to identify opportunities for optimizations that reduce cost, improve efficiency and ensure scalability.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning - AI

Observability refers to the ability to understand the internal state and behavior of a system by analyzing its outputs, logs, and metrics. Although the implementation is straightforward, following best practices is crucial for the scalability, security, and maintainability of your observability infrastructure.

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How DPG Media uses Amazon Bedrock and Amazon Transcribe to enhance video metadata with AI-powered pipelines

AWS Machine Learning - AI

As DPG Media grows, they need a more scalable way of capturing metadata that enhances the consumer experience on online video services and aids in understanding key content characteristics. Word information lost (WIL) – This metric quantifies the amount of information lost due to transcription errors.

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Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

AWS Machine Learning - AI

Without a scalable approach to controlling costs, organizations risk unbudgeted usage and cost overruns. This scalable, programmatic approach eliminates inefficient manual processes, reduces the risk of excess spending, and ensures that critical applications receive priority. However, there are considerations to keep in mind.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

Under Input data , enter the location of the source S3 bucket (training data) and target S3 bucket (model outputs and training metrics), and optionally the location of your validation dataset. To do so, we create a knowledge base. For Job name , enter a name for the fine-tuning job.