Wed.Jul 10, 2024

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Meta’s approach to machine learning prediction robustness

Engineering at Meta

Meta’s advertising business leverages large-scale machine learning (ML) recommendation models that power millions of ads recommendations per second across Meta’s family of apps. Maintaining reliability of these ML systems helps ensure the highest level of service and uninterrupted benefit delivery to our users and advertisers. To minimize disruptions and ensure our ML systems are intrinsically resilient, we have built a comprehensive set of prediction robustness solutions that ensure stability w

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Introducing Cloudera Observability Premium

Cloudera Blog

There’s nothing worse than wasting money on unnecessary costs. In on-premises data estates, these costs appear as wasted person-hours waiting for inefficient analytics to complete, or troubleshooting jobs that have failed to execute as expected, or at all. They manifest as idle hardware waiting for urgent workloads to come in, ensuring sufficient spare capacity to run them amidst noisy neighbors and resource-hungry, lower-priority workloads.

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Taming the tail utilization of ads inference at Meta scale

Engineering at Meta

Tail utilization is a significant system issue and a major factor in overload-related failures and low compute utilization. The tail utilization optimizations at Meta have had a profound impact on model serving capacity footprint and reliability. Failure rates, which are mostly timeout errors, were reduced by two-thirds; the compute footprint delivered 35% more work for the same amount of resources; and p99 latency was cut in half.

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MiFID II: Data Streaming for Post-Trade Reporting

Confluent

Data streaming with Confluent enables the integration and processing of post-trade data in real time, allowing for compliance with MiFID II. Learn how.

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NAN068: Is Cloud Networking the Future for Network Engineers?

Packet Pushers

On today’s episode, host Eric Chou and guest Kyler Middleton discuss the transition from on-prem network engineering to cloud networking; the importance of adapting to new platforms such as AWS, Azure, and Terraform; and the future of cloud versus on-premises solutions. We also discuss Kyler’s background, navigating the journey from a farm upbringing to a.

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Lesson 191 - The Entity Trap (July 15, 2024)

Developer to Architect

In Lesson 190 I talked about the difference between a logical and physical architecture. In this lesson I demonstrate an anti-pattern called The Entity Trap when identifying logical components and creating a logical architecture. I talk about why this approach is bad and the negative consequences of architectures created using this technique.

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Identity, Security, Access: Three Reasons Why Enterprises Need Zero Trust

Dataversity

Zero trust is taking the enterprise by storm. About two-thirds (63%) of organizations worldwide have fully or partially implemented the cybersecurity posture, Gartner reports, following the motto of “never trust, always verify.” This rush to zero trust makes sense in the remote age. The proliferation of anywhere users means it’s harder than ever to lock down […] The post Identity, Security, Access: Three Reasons Why Enterprises Need Zero Trust appeared first on DATAVERSITY.