Description
Machine Learning at Scale offers a comprehensive program designed to elevate Machine Learning Engineers to a 'x10' performance level. Through weekly high-quality insights, the platform delves into the complexities of building and deploying machine learning systems at scale. The content is curated by Ludo, a Machine Learning Engineer at Google with extensive experience in large-scale ML systems, including fighting abuse across billions of users at 500k QPS, pretraining transformer-based models, and working on end-to-end YouTube Ads systems.
Read More
The curriculum covers critical areas such as MLSys case studies, design patterns, and ML-career advice. Specific deep dives are planned for Machine Learning Systems, LLM Inference at Scale, Recommendation Systems at Scale, Search & Ranking Systems (RAG), Search in the AI era, and Ads Systems at Scale. This focus ensures engineers gain practical knowledge applicable to real-world challenges in modern AI development.
Subscribers benefit from practical advice and insights derived from hands-on experience at leading tech companies. The platform aims to demystify the intricacies of ML systems, enabling engineers to design, implement, and optimize solutions that handle massive datasets and high query volumes. The target audience includes aspiring and practicing Machine Learning Engineers who want to enhance their skills, understand best practices, and advance their careers in the rapidly evolving field of AI.
Machine Learning at Scale provides a unique opportunity to learn from an industry expert who has applied ML techniques at CERN and developed computer vision theses at Volvo. The program is structured to provide actionable knowledge, helping engineers unlock their full potential and become more effective and impactful in their roles. The upcoming YouTube channel and MLSys course will further expand the learning resources available to the community.
- CategoryAI News Readers & Aggregators
What You'll Get
Weekly high-quality insights for ML engineers
Focus on large-scale Machine Learning Systems (MLSys)
MLSys Case Studies and Design Patterns
Machine Learning Career Advice
Deep dives into LLM Inference at Scale
Expertise in Recommendation Systems at Scale
Guidance on Search & Ranking Systems (RAG)
Insights into Ads Systems at Scale
Content from a Google Machine Learning Engineer
Practical experience with high QPS systems
Transformer-based model training and fine-tuning
Computer Vision thesis development
Application of ML at CERN
Who Machine Learning at Scale Is Best For
- ML System Design
- LLM Deployment
- Recommendation Engines
- Search and Ranking
- Ad Systems Optimization
- Career Development
- Large-Scale Data Processing
