Machine learning at scale

What is Machine Learning at Scale?

Machine Learning at Scale is an educational resource and course designed to help Machine Learning engineers enhance their skills. It offers weekly insights, tools, and deep dives into large-scale ML systems, transformer-based models, and real-world applications like ad systems and computer vision. Created by a Google ML engineer, it focuses on practical, high-impact learning for scaling ML solutions.

Who will use Machine Learning at Scale?

Machine Learning at Scale is ideal for aspiring or experienced Machine Learning engineers, data scientists, and tech professionals aiming to master large-scale ML systems. It’s particularly valuable for those working with high-throughput systems (e.g., 500k QPS), transformer models, or ads/computer vision applications, as well as learners seeking industry-proven expertise from a Google ML engineer.

How to use Machine Learning at Scale?

• Subscribe to receive weekly high-quality insights and updates.
• Explore deep dives into tools and techniques used by ML engineers.
• Apply practical knowledge to projects involving large-scale systems or transformer models.
• Leverage case studies (e.g., YouTube Ads, CERN) to understand real-world ML scaling challenges.
• Engage with content tailored to boost productivity and expertise in ML engineering.

In what environments or scenarios is Machine Learning at Scale suitable?

Machine Learning at Scale is perfect for professionals tackling high-volume data (e.g., billion-user systems), ads targeting, or computer vision projects. It’s also suited for academic or industrial research (like particle physics at CERN) and engineers optimizing ML pipelines for performance, scalability, or abuse detection in tech-driven environments.