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This is a guest post by Ankit Sirmorya. Ankit is working as a Machine Learning Lead/Sr. Machine Learning Engineer at Amazon and has led several machine-learning initiatives across the Amazon ecosystem. Ankit has been working on applying machine learning to solve ambiguous business problems and improve customer experience. For instance, he created a platform for experimenting with different hypotheses on Amazon product pages using reinforcement learning techniques. Currently, he is in the Alexa Shopping organization where he is developing machine-learning-based solutions to send personalized reorder hints to customers for improving their experience.
This is a guest post by Ankit Sirmorya. Ankit is working as a Machine Learning Lead/Sr. Machine Learning Engineer at Amazon and has led several machine-learning initiatives across the Amazon ecosystem. Ankit has been working on applying machine learning to solve ambiguous business problems and improve customer experience. For instance, he created a platform for experimenting with different hypotheses on Amazon product pages using reinforcement learning techniques. Currently, he is in the Alexa Shopping organization where he is developing machine-learning-based solutions to send personalized reorder hints to customers for improving their experience.
Design a location-based social search application similar to Tinder which if often used as a dating service. It allows users to use a swiping motion to like (swipe right) or dislike (swipe left) other users, and allows users to chat if both parties like each other(a “match”).
This is a guest post by Ankit Sirmorya. Ankit is working as a Machine Learning Lead/Sr. Machine Learning Engineer at Amazon and has led several machine-learning initiatives across the Amazon ecosystem. Ankit has been working on applying machine learning to solve ambiguous business problems and improve customer experience. For instance, he created a platform for experimenting with different hypotheses on Amazon product pages using reinforcement learning techniques. Currently, he is in the Alexa Shopping organization where he is developing machine-learning-based solutions to send personalized reorder hints to customers for improving their experience.
Design a photo-sharing platform similar to Instagram where users can upload their photos and share it with their followers. Subsequently, the users will be able to view personalized feeds containing posts from all the other users that they follow.
The application should be able to support the following requirements.
This is a guest post by Ankit Sirmorya. Ankit is working as a Machine Learning Lead/Sr. Machine Learning Engineer at Amazon and has led several machine-learning initiatives across the Amazon ecosystem. Ankit has been working on applying machine learning to solve ambiguous business problems and improve customer experience. For instance, he created a platform for experimenting with different hypotheses on Amazon product pages using reinforcement learning techniques. Currently, he is in the Alexa Shopping organization where he is developing machine-learning-based solutions to send personalized reorder hints to customers for improving their experience.
Design an instant messenger platform such as WhatsApp or Signal which users can utilize tosend messages to each other. An essential aspect of the application is that the chat messageswon’t be permanently stored in the application.
FUN FACT: Some of the chat messengers such as FB Messenger stores the chat messages unless the users explicitly delete it. However, instant messengers such as WhatsApp don’t save the messages permanently on their server.
This is a guest post by Ankit Sirmorya. Ankit is working as a Machine Learning Lead/Sr. Machine Learning Engineer at Amazon and has led several machine-learning initiatives across the Amazon ecosystem. Ankit has been working on applying machine learning to solve ambiguous business problems and improve customer experience. For instance, he created a platform for experimenting with different hypotheses on Amazon product pages using reinforcement learning techniques. Currently, he is in the Alexa Shopping organization where he is developing machine-learning-based solutions to send personalized reorder hints to customers for improving their experience.
Design a video streaming platform similar to Netflix where content creators can upload their video content and viewers are able to play video on different devices. We should also be able to store user statistics of the videos such as number of views, video watched duration, and so forth.
Today I have the pleasure of announcing my new app—Max reHIT Workout—on Product Hunt. Max reHIT Workout is an exercise app that guides you through interval workouts.
I won’t pitch the app here. I'll just say I’m proud of how it turned out and if you want an optimal algorithm for exercising, you might like it.
I know I haven’t been writing much lately. That's because there’s been very little evolution in software system architecture. It’s pretty much same thing, different day. In many ways that’s good, but it’s not interesting to write about.
This article, while definitely self serving, targets the choice of using a native iOS environment versus a cloud environment for an app. It’s a choice every developer must make. How do you make that choice? What are the implications? What choice would I make next time?
What would a totally new search engine architecture look like? Who better than Julien Lemoine, Co-founder & CTO of Algolia, to describe what the future of search will look like. This is the second article in a series. Here's Part 1.
Search engines need to support fast scaling for both Read and Write operations. Rapid scaling is essential in most use cases. For example, adding a vendor in a marketplace generates a spike of indexing operations (Write), and a marketing campaign generates a spike of queries (Read). In most use cases, both Read and Write operations scale but not at the exact same moment. The architecture needs to handle efficiently all these situations as the scaling of Read and Write operations varies over time in most use cases.
Until now, search engines were scaling with Read and Write operations colocated on the same VMs. This scaling method brings drawbacks, such asWrite operations unnecessarily hurting the Read performance and using a significant amount of duplicated CPU at indexing. This article explains those drawbacks and introduces a new way to scale more quickly and efficiently by splitting Read and Write operations.
Hey, HighScalability is here again!
The circulatory system of the internet. @tylermorganwall
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Sorry for the long gap in posting, but I’ve been building a new app. I’m looking for testers for my new iOS fitness app: Max reHIT Workout. It guides you through proven reduced-exertion high-intensity interval workouts. If that interests you, please give it a try through TestFlight. I’d appreciate any feedback and suggestions for improvement. Thanks!
Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...
What would a totally new search engine architecture look like? Who better than Julien Lemoine, Co-founder & CTO of Algolia, to describe what the future of search will look like. This is the first article in a series.
Search engines, and more generally, information retrieval systems, play a central role in almost all of today’s technical stacks. Information retrieval started in the beginning of computer science. Research accelerated in the early 90s with the introduction of the Text REtrieval Conference (TREC). After more than 30 years of evolution since TREC, search engines continue to grow and evolve, leading to new challenges.
In this article, we look at some key milestones in the evolution of search engine architecture. We also describe the challenges those architectures face today. As you’ll see, we grouped the engines into four architecture categories. This is a simplification, as there are in reality a lot of different engines with various mix of architectures. We did this to focus our attention on the most important characteristics of those architectures.
Hey, it's HighScalability time!
Not your style? This is completely different. No, it’s even more different than that.
Today in things that nobody stopped me from doing:
— Forrest Brazeal (@forrestbrazeal) May 28, 2021
The AWS Elastic Load Balancer Yodel Rag. pic.twitter.com/ocyVLf8WlU
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Do employees at your company need to know about the cloud? My book will teach them all they need to know. Explain the Cloud Like I'm 10. On Amazon it has 307 mostly 5 star reviews. Here's a 100% keto paleo low carb carnivore review: