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Automatic Feature Engineering: An Event-Driven Approach

Dataiku

TL;DR : As data science evolves as a discipline, we think that it’s time to have a more systematic approach to feature engineering. That’s why we built EventsAggregator , an automatic feature engineering framework that leverages user’s knowledge to generate expressive features.

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Introducing Honeycomb for Kubernetes: Bridging the Divide Between Applications and Infrastructure

Honeycomb

This fragmented approach is not only painful but overlooks a crucial reality: infrastructure impacts application performance, and vice versa. Our approach: More than just metrics At Honeycomb, observability isn’t just about collecting data; it’s about making that data actionable in meaningful ways.

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Comprehensive Exploration on Understanding Datadog

Perficient

Introduction This guide aims to unravel the features that Datadog brings, providing users with a robust platform for navigating the complexities of modern IT environments. Key Features Explored Integrations Datadog’s integration ecosystem is a standout feature, boasting over 650 officially listed integrations.

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Microsoft Azure OpenAI Service and DataRobot Modernize Data Science Work with Cutting-Edge Technology Innovations

DataRobot

However, we’re now taking the information managed by DataRobot (such as the data , features , models , predictions ) and leveraging the capabilities of the Azure OpenAI Service to make it more accessible and understandable. An example of this is proposing why a feature has a high impact on predictions.

Azure 98
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React Norway Conference 2022

Xebia

I arrived a day early because the event would start at 8am, so travelling from The Netherlands could be a problem. As my first impression arriving at the Farris bad hotel I saw a giant banner of the event with beautiful illustration and the hotel hall has an astonishing view to the beach. Request a new feature for the team.

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Guide to SaaS Marketing: 12 Strategies for 2023

Altexsoft

The goal of SaaS marketing is to attract and retain customers by effectively communicating the value of the product, educating them on its features and benefits, and providing an outstanding user experience that encourages ongoing use and loyalty. The only difference is in the approach.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning - AI

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.