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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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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.

Education 363
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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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Using Graph Processing for Kafka Stream Visualizations

Confluent

Stream processing engines like KSQL furthermore give you the ability to manipulate all of this fluently. We will cover how you can use them to enrich and visualize your data, add value to it with powerful graph algorithms, and then send the result right back to Kafka. Step 2: Using graph algorithms to recommend potential friends.

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Natural Language Processing: A Guide to NLP Use Cases, Approaches, and Tools

AltexSoft

And this technology of Natural Language Processing is available to all businesses. Available methods for text processing and which one to choose. What is Natural Language Processing? Natural language processing or NLP is a branch of Artificial Intelligence that gives machines the ability to understand natural human speech.

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Rebuilding Netflix Video Processing Pipeline with Microservices

Netflix Tech

This introductory blog focuses on an overview of our journey. Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process. Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process.

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

Snowflake

This is done by combining parameter preserving model rewiring with lightweight fine-tuning to minimize the likelihood of knowledge being lost in the process. You can learn more in our SwiftKV research blog post. SwiftKV achieves higher throughput performance with minimal accuracy loss (see Tables 1 and 2).