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Automating Relevance Tuning for Event Search

Eventbrite Engineering

1 Background Relevance tuning is the process of making incremental changes to a search algorithm to improve the ranking of search results to better meet the information needs and preferences of users. Authored by: Zelal Gungordu and Delaine Wendling.

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Revolutionizing Real-Time Streaming Processing: 4 Trillion Events Daily at LinkedIn

LinkedIn Engineering

Authors: Bingfeng Xia and Xinyu Liu Background At LinkedIn, Apache Beam plays a pivotal role in stream processing infrastructures that process over 4 trillion events daily through more than 3,000 pipelines across multiple production data centers.

Process 119
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Easy and Secure LLM Inference and Retrieval Augmented Generation (RAG) Using Snowflake Cortex

Snowflake

To reduce these AI hallucinations, LLMs can be combined with private data sets via processes that either don’t require LLM customization (such as prompt engineering or retrieval augmented generation) or that do require customization (like fine-tuning or retraining). Let’s dive into the details and check out some code along the way.

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Azure Monitor vs Azure Advisor: Key Differences & Similarities

Knowledge Hut

Thus, this KnowledgeHut blog will show us how to fine-tune our Azure environment. Automation Supports automation through Azure Logic Apps, Azure Functions and Azure Monitor Action Groups. Offers Azure Policy integration to automate resource configuration. Let’s get started! Also aids compliance enforcement.

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Language Models, Explained: How GPT and Other Models Work

AltexSoft

GPT-3 has a spin-off called ChatGPT that is specifically fine-tuned for conversational tasks. Since the relevant piece of information here is Jenny , the most probable pronoun is she or her. As a part of conversational AI systems, language models can provide relevant text responses to inputs. Pretty cool, right?

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Highest Paying Java Developer Jobs in Singapore in 2023

Knowledge Hut

Undoubtedly, companies prefer veteran candidates over inexperienced ones as it is pretty easy for employers to believe that seasoned candidates would be well-versed with the required skills and workflow. However, that doesn't imply that freshers or entry-level Software Developers can't get a job in their dream company.

Java 52
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ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

Netflix Tech

It featured three relevant talks from LinkedIn, Netflix and Facebook, and a platform architecture overview talk from first time participant Dropbox. He started by laying out some of the challenges around inefficiencies of engineering time when manually optimizing for weights/parameters in their business objective functions.