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Enabling privacy and choice for customers in data system design

Lacework

When architecting data systems, a key philosophy is keeping customer privacy front and center both in the design choices made and and options presented to the user, while ensuring the ability to meet business needs and service criteria. What regional data requirements or preferences should be considered?

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How ML System Design helps us to make better ML products

Xebia

With the industry moving towards end-to-end ML teams to enable them to implement MLOPs practices, it is paramount to look past the model and view the entire system around your machine learning model. The classic article on Hidden Technical Debt in Machine Learning Systems explains how small the model is compared to the system it operates in.

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Revenue Management Systems for Hotels: Products and Features

Altexsoft

A solid revenue management system (RMS) serves as a trusted steering wheel, guiding in the right direction and making that journey smoother. From analyzing real-time data to making forecasts about future trends, these systems provide functionality for staying ahead in a dynamic hospitality market. Coordinated pricing adjustments.

Hotels 52
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Simulation Theory, Observability, and Modern Software Practices

Honeycomb

The real vs. simulated systems In Baudrillard’s terms, the authentic experiences and the real have been replaced by symbols and signs ( logs , metrics , traces ). In software development, the real can be seen as the initial design and requirement specifications—the ideal system as imagined by its creators.

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Announcing Prisma Cloud AI-SPM: Protect Your Complete AI Stack

Prisma Clud

AI-SPM takes elements from existing security posture management approaches like data security posture management (DSPM) , cloud security posture management (CSPM), and cloud infrastructure entitlement management ( CIEM ), and adapts them to address the specific challenges of AI systems in production environments.

Cloud 59
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How Cloud Security Influences IoT Security

Xebia

So, let’s talk more about what are the issues that cloud systems that handle IoT devices face and what are the potential solutions to them. However, the same level of security improvements have not been done on the backend systems monitoring and maintaining these devices. . The cloud services behind the devices are not.

IoT 130
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In AI we Trust? Why we Need to Talk about Ethics and Governance (part 1 of 2)

Cloudera

As the systems we develop become increasingly sophisticated, and in some cases autonomous, we remain ethically responsible for those systems. This includes systems based on AI and ML. Ethical AI is a multi-disciplinary effort to design and build AI systems that are fair and improve our lives.