Understand model techniques along with the best practices in applying ML models in production at scale.

Deep Dive into System Design Interview

Machine learning system design is an important component of any machine learning interview. The ability to address problems, identify requirements, and discuss trade-offs can help us stand out among hundreds of other candidates. This module will discuss model techniques, along with best practices in applying scalable machine learning models in production. Once we're done with this module, we’ll be able to apply and leverage knowledge from top researchers at tech companies. We’ll have an up-to-date knowledge of model techniques from hundreds of the latest research and industry papers. We might even be able to impress interviewers once we’ve gone through the lessons of this module.

Machine Learning System Design

Congratulations, you’ve successfully finished the module “Machine Learning System Design.”

## Summary
Starting with the basics of machine learning, we learned about feature selection and engineering, training pipeline, inference, and metrics evaluation. This knowledge will help you stand out in interviews. 

## Takeaways
- We reviewed the fundamentals of designing distributed systems.
- We got an overview of how to design the architecture of scalable web applications.
- We revisited the concepts required for designing a system.
- We got a brief overview of the system design building blocks on which a scalable system stands
- We brushed up our system design skills by applying what we learned in this Path to solve real-world system design problems, such as designing YouTube, Google Maps, Google Docs, Uber, WhatsApp, and so on.
- We learned how to design systems based on machine learning.

Congratulations! Now you are ready for system design interviews.

Congratulations, you’ve successfully finished the module “Machine Learning System Design.”

# Summary
Starting with the basics of machine learning, we learned about feature selection and engineering, training pipeline, inference, and metrics evaluation. This knowledge will help you stand out in interviews. 

# Takeaways
- We reviewed the fundamentals of designing distributed systems.
- We got an overview of how to design the architecture of scalable web applications.
- We revisited the concepts required for designing a system.
- We got a brief overview of the system design building blocks on which a scalable system stands
- We brushed up our system design skills by applying what we learned in this Path to solve real-world system design problems, such as designing YouTube, Google Maps, Google Docs, Uber, WhatsApp, and so on.
- We learned how to design systems based on machine learning.

Congratulations! Now you are ready for system design interviews.

Here's a quick summary of the "Machine Learning System Design" module.

Machine Learning Primer

Video Recommendation

Feed Ranking

Ad Click Prediction

Rental Search Ranking

Estimate Food Delivery Time

Machine Learning Knowledge

Machine Learning Model Diagnosis

Conclusion

Final Remarks

Summary