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Bring Your Own Algorithm to Anomaly Detection

Pinterest Engineering

Charles Wu | Software Engineer; Isabel Tallam | Software Engineer; Kapil Bajaj | Engineering Manager Overview In this blog, we present a pragmatic way of integrating analytics, written in Python, with our distributed anomaly detection platform, written in Java. The execution flow of one anomaly detection job, defined by one JSON job spec.

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What are the Commonly Used Machine Learning Algorithms?

Knowledge Hut

There is no end to what can be achieved with the right ML algorithm. Machine Learning is comprised of different types of algorithms, each of which performs a unique task. U sers deploy these algorithms based on the problem statement and complexity of the problem they deal with.

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An educational side project

The Pragmatic Engineer

for the simulation engine Go on the backend PostgreSQL for the data layer React and TypeScript on the frontend Prometheus and Grafana for monitoring and observability And if you were wondering how all of this was built, Juraj documented his process in an incredible, 34-part blog series. You can read this here. Serving a web page.

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The Top Pinterest Engineering Blog posts from 2023

Pinterest Engineering

Our Pinterest Engineering Blog goes deeper into the technical learnings and insights behind many of these launches. Here, you’ll be the first to know about new Engineering blogs, events and employee stories. Thank you for supporting our Pinterest Engineering Blog this year. In 2023, we launched Pinterest Engineering on LinkedIn!

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The Quest to Understand Metric Movements

Pinterest Engineering

This blog outlines the three pragmatic approaches that form the basis of the root-cause analysis (RCA) platform at Pinterest. How we are analyzing the metric segments takes inspiration from the algorithm in Linkedins ThirdEye. a new recommendation algorithm). The possible reasons go on andon.

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PyTorch Introduction — Training a Computer Vision Algorithm

DareData

With its capabilities of efficiently training deep learning models (with GPU-ready features), it has become a machine learning engineer and data scientist’s best friend when it comes to train complex neural network algorithms. In this blog post, we are finally going to bring out the big guns and train our first computer vision algorithm.

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SwiftKV Cuts LLM Inference Costs by 75% with Snowflake Cortex AI

Snowflake

You can learn more in our SwiftKV research blog post. It offers a high-quality, user-friendly synthetic data generation pipeline and a scalable, adaptable training framework for algorithmic innovation, as well as an out-ofthe-box recipe for training your own SwiftKV models.