Q.Who is the author of Machine Learning at Scale?
A.The author is Ludo, a Machine Learning engineer at Google.
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Machine learning at scale
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💲Free
Machine Learning at Scale is a valuable resource for machine learning engineers looking to enhance their skills and knowledge. It offers weekly insights, deep dives into ML topics, and features tools used by ML engineers, all aimed at helping users become more proficient in their field.
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Platform
web
LLM OptimizationLLM TrainingLLMsML EngineeringML System DesignML SystemsMachine Learning
What is Machine learning at scale?
Machine Learning at Scale is a Substack publication offering weekly insights on machine learning systems and engineering at scale. It provides high-quality content to help machine learning engineers upskill and become more proficient in their field.
Core Technologies
- Machine Learning
- LLMs
- RAG Systems
- LLM Optimization
- LLM Training
- ML System Design
Key Capabilities
- Delivers weekly insights
- Covers ML systems and engineering
- Features tools used by ML engineers
- Offers deep dives into ML topics
- Provides an archive of past articles
Use Cases
- Upskilling as a Machine Learning engineer
- Learning about ML systems at scale
- Staying updated on the latest ML tools and techniques
- Understanding ML system design principles
Core Benefits
- Enhances machine learning skills
- Provides high-quality, curated content
- Focuses on practical, real-world applications
- Covers a range of important ML topics
- Offers insights from a Google ML engineer
Key Features
- Weekly newsletter with high-quality insights
- Deep dives into ML topics
- Tools used by Machine Learning engineers
- Archive of past articles
How to Use
- 1Subscribe to the Substack publication
- 2Receive weekly insights in your inbox
- 3Access the archive of past articles
- 4Explore upcoming resources like the ML System design course and YouTube channel
Frequently Asked Questions
Q.What topics are covered in Machine Learning at Scale?
A.The publication covers topics such as RAG systems, LLM optimizations, LLM training, ML System design, and tools used by Machine Learning engineers.
Q.How often is the newsletter delivered?
A.The newsletter is delivered once a week.
Q.Is there a cost to subscribe to Machine Learning at Scale?
A.The provided text does not explicitly mention a cost, but it is a Substack publication, which often involves a free or paid subscription model. Please refer to the website for more details.
Pros & Cons (Reserved)
✓ Pros
- High-quality, curated content
- Insights from a Google ML engineer
- Focus on practical, real-world applications
- Covers a range of important ML topics
✗ Cons
- Content is primarily text-based (for now)
- ML System design course and YouTube channel are still in development
- Requires a Substack subscription
Alternatives
No alternatives found.