E 24- Scaling Machine Learning Systems | Younes Abou-Elnagah | إلى البيانات و ما بعدها

ازاي الشركات الكبيرة ب Scale Machine Learning Systems عشان تقدر تخدم ملايين من المستخدمين
Refernces & Supplemental material for Scaling Machine Learning Systems
docs.google.com/document/d/1I...
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1. Introduction to Scaling ML Systems:
- Why is scaling important in the context of machine learning?
2. Challenges in Scaling:
- What are the common challenges organizations face when scaling their machine-learning systems?
- How do these challenges differ from scaling traditional software systems?
3. Data Scaling:
- How do the volume, velocity, and variety of data impact the scaling of machine learning systems?
- What strategies can be employed to handle large and diverse datasets efficiently?
4. Model Scaling:
- As models become more complex, what considerations are crucial for scaling machine learning models?
- How do we decide whether to scale up (bigger models) or scale out (distributed computing)?
5. Infrastructure and Resources:
- The roles infrastructure plays in scaling ML systems, and how do resource considerations change with scale
6. Scaling Training Pipelines:
- How can organizations scale their model training pipelines to handle large datasets and complex models?
- What are some best practices for distributed training in machine learning?
7. Operational Considerations:
- Once a model is trained, what operational challenges arise when deploying and managing it at scale?
- How do you address issues such as monitoring, logging, and maintaining model performance in production?
8. Scalability and Performance Metrics:
- What key metrics should organizations track to ensure the scalability and performance of their machine-learning systems?
- How do these metrics evolve as systems scale?
9. Data Privacy and Security:
- How does scaling impact data privacy concerns, and what measures should be taken to ensure secure scaling of ML systems?
- Are there specific considerations for handling sensitive information at scale?
10. Future Trends in Scaling ML:
- What emerging technologies or methodologies do you see influencing the future of scaling machine learning systems?
- How are advancements in hardware, software, or algorithms shaping the scalability landscape?
11. Advice for Practitioners:
- Advice for individuals or teams looking to scale their machine-learning systems
- Are there common pitfalls or misconceptions that should be avoided?
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Contact Information:
Younes Abou Elnaga LinkedIn: / younosnaga
Youssef Hosni LinkedIn: / youssef-hosni-b2960b135