How does Uber predict Arrival Times (ETA) for trips? | Uber ML System Design | #systemdesign

Do you know how cab companies like Uber, Ola, and Lyft predict the Expected Time of Arrival (ETA) for the trips? In this video, we design an end-to-end machine learning system that incrementally improves the system's prediction capabilities. We also discuss how the ETA prediction service is integrated into the overall Uber system design.

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Tags: system design,interview preparation,interviews,software interview,coding interview,programming,gaurav sen,system design interview,grokking the system design interview,cracking the coding interview,sudocode,uber,lyft,ola,predict arrival time,predict eta, eta

[DevSense]

It will help us stay motivated and we will be able to bring this type of content more often. By being a member, you will also get to attend monthly virtual meetups with our team where you can discuss about machine learning, system design, career growth, motion graphics, and many other things. 🙏

[YashDarak-zw]

This video showcases the basics of building an actual production grade model in simple terms. Great explanation, thank you!

[EduAnmoldeep]

Super valuable gold mine!!!
Super rich value will visit again after pondering in the learnings!

Forever in gratitude to DevSense team and Ashutosh!

[akshitbansal]

Ashutosh, this is phenomenal work! This is the type of content that a young professional like me in the field of ML loves to see! Making complex concepts so easy to understand, thank you so much for doing this!
Would love to see more such videos please!

[TechKidDiary]

Your youtube channel has a very interesting content. I really liked this video of Uber predicting their ETA. I think videos like this will resonate a lot with LinkedIn's ML community.

[priyabratamishra]

Very insightful. Please keep posting these kind of videos.

[rajbhowmick]

This is absolutely crazy. Just the video we all needed. The best part about this is, it is just like a story with all the information that is required. Good job AH !

[mdmusaddique_cse]

Very insightful. Glad to find this channel.

[tanviranga]

This is an awesome content! Thanks for explaining this concept so smoothly with very cool animation.

[pavanpandya]

Loved it. You made it easy & simple to understand.

[pokerface]

This is really well explained! Thanks for sharing.

[aakashsingh]

Well articulated video. Keep up the great work

[GabrielPontes-phru]

high quality content! thanks for sharing it!

[AdvikaThakur-fl]

Loved it! Now, waiting for the next-"Instagram"

[anishhota]

Insightful and concise video. Keep posting the MLSD videos.

I have a doubt though, why not use the "type of scenario features" in the beginning along with the discrete( spelling was wrong in the video) features rather than bringing the feature at the end of the architecture?

[gauravfotedar]

Can you elaborate on how the embedding is created from sparse features? Or if you have a link I can read from.

[harshvaidya]

Great explainer video. Could you tell which tool you used to put this together. The animation and slide movements are on point with audio