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Machine learning at scale

Machine learning at scale: Learn about ML systems from top tech companies. Delivered once a week in your inbox.

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Why choose Machine learning at scale?

Core Summary

Machine learning at scale is a subscription-based knowledge platform dedicated to helping Machine Learning engineers upskill, with the goal of making them a x10 Machine Learning Engineer. The platform is run by Ludovico Bessi (Ludo), a Machine Learning engineer at Google, and is already subscribed to by over 6000 Machine Learning engineers from leading companies like Google, Amazon, YouTube, and Microsoft.

Main Content & Features

  • Anatomy of ML Systems: Deep dive into the internal structure and components of machine learning systems.
  • Patterns of ML Systems: Explore common patterns and best practices in ML system design.
  • ML Systems Case Studies: Analyze the construction and optimization of large-scale ML systems through real-world cases.

Upcoming Deep Dives

  • LLM training: Large Language Model training.
  • LLM Inference At Scale: Large-scale LLM inference.
  • Recommendation Systems at Scale: Large-scale recommendation systems.
  • Computer Vision Systems in Production: CV systems in production environments.
  • Search & Ranking Systems: Search and ranking systems.
  • MLSys course: Machine Learning Systems course.

Target Users

  • Machine Learning engineers looking to upskill and unlock their full potential.
  • Businesses looking for AI help, specifically in Retrieval, Ranking, Recommendation systems, and LLM integrations.

Core Advantages & Author Background

Content is provided by an expert with rich frontline practical experience. Ludovico Bessi's key achievements include:

  • Worked with large scale ML systems to fight abuse across billions of users at 500k QPS at Google.
  • Pretrained and finetuned transformer-based models to understand user behaviour.
  • Working with the end-to-end YouTube Ads systems from Ads selection to formats.
  • Making YouTube the best video shopping destination on the planet by building better Recommendation systems.
  • Applied Machine Learning techniques at CERN to understand particle interactions.
  • Developed computer vision thesis based on Transformers at Volvo.

Usage & Pricing

  • Users can subscribe via Substack to get weekly high-quality insights, currently offered for free.
  • Businesses seeking AI help (specializing in Retrieval, Ranking, Recommendation systems, and LLM integrations) can reach out via LinkedIn for a free initial consultation.

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