Gain insights into ML system design, state-of-the-art techniques, and best practices for scalable production. Learn from top researchers and stand out in your next ML interview.

Machine Learning System Design is an important component of any ML interview. The ability to address problems, identify requirements, and discuss tradeoffs helps you stand out among hundreds of other candidates. Readers of this course able to get offers from Snapchat, Facebook, Coupang, Stitchfix and LinkedIn. 

This course will help you understand the state of the practice on model techniques along with best practices in applying ML models in production at scale. Once you finished the course you can learn more use-cases at: http://mlengineer.io/

Once you're done with the course, you will be able to apply and leverage knowledge from top researchers at tech companies. You will have up to date knowledge in model techniques from hundreds of the latest research and industry papers. There is even a chance that the interviewers will be surprised at the depth of your knowledge.

I really found the quizzes very helpful for testing my ML understanding. Also, the resources shared helped me a lot for revising concepts for my interview preparation. This course will definitely help engineers crack Machine Learning Engineering and Data Science interviews

Senior Data Scientist at Amazon

I have been using your github repo to prep for my interviews and got an offer with NVIDIA with their data science team. Thanks again for your help!

Data Scientist at NVIDIA

I really like what you've built, it'll help a lot of engineers.

MLE at Facebook

It's well organized and the illustrations are well done. Being able to visualize and walk through the steps in order is really helpful in system design. The hints for quizzes is a nice addition, a hint is given if you get stuck in a real interview, mimicking how a real interview 

DS at Fortune 500

Andrew

I just heard back from the recruiter that I passed the Google L5 HC. Thank you very much for sharing the resources on GitHub and for the course on educative.io!

Google Machine Learning Engineer, L5

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Senior Machine Learning Engineer, Intuit

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Senior Machine Learning Engineer at Cruise

Machine Learning System Design


# 3. Model

## Feature engineering
|**Features**   |**Feature engineering**   |**Description**   |
|:-: |:-: |:-: |
|AdvertiserID   | Use Embedding or feature hashing   |It's easy to have millions of advertisers   |
|User’s historical behavior, i.e., numbers of clicks on ads over a period of time.   | Feature scaling, i.e., normalization  ||
|Temporal: time_of_day, day_of_week etc   | One hot encoding   ||
|Cross features   |Combine multiple features   |See example in the Machine Learning System Design Primer  |

## Training data

Before building any ML models we need to collect training data. The goal here is to collect data across different types of posts while simultaneously improving the user experience. As you recall from the previous lesson about the waterfall model, we can collect a lot of data about ad clicks. We can use this data for training the Ad Click model. 

We can start to use data for training by selecting a period of data: last month, last six months, etc. In practice, we want to find a balance between training time and model accuracy. We also downsample the negative data to handle the imbalanced data. 

## Model

### Selection


- We can use deep learning in distributed settings. We can start with fully connected layers with the Sigmoid activation function applied to the final layer. Because the CTR is usually very small (less than 1%), we would need to resample the training data set to make the data less imbalanced. It’s important to leave the validation and test sets intact to have accurate estimations about model performance.
 




Learn about the Ad prediction model architecture.

Machine Learning Primer

Video Recommendation

Feed Ranking

Ad Click Prediction

Rental Search Ranking

Estimate Food Delivery Time

Conclusion

Machine Learning Knowledge

Machine Learning Model Diagnosis

Ad Click Prediction Model

3. Model

Feature engineering

Features	Feature engineering	Description
AdvertiserID	Use Embedding or feature hashing	It’s easy to have millions of advertisers
User’s historical behavior, i.e., numbers of clicks on ads over a period of time.	Feature scaling, i.e., normalization
Temporal: