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Algolia raises $110M for its search-as-a-service

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Re: Algolia raises $110M for its search-as-a-service

#111

Post by mc3 »

redox_ wrote: post #110
a_imho wrote: post #80

Earlier quoted context omitted.

search for e.g 'microsoft' -> ~120k results change sort by popularity to sort by date -> ~30k results I don't really understand how sorting can affect the number of results. Btw youtube search does this too.

Yes, when sorting by date it's currently configured to disable typo-tolerance. This is to avoid having an old (but approximative, with typos) result BEFORE the correct ones.

Ah the old multi-factor sort problem. Fun figuring out how to handle those scenarios in Elastic Search too (or more generally any sorting UI). For example you want traditional Chinese food near you. What is better - traditional Chinese food 14km away or fusion 2km away? Well that depends on the wetware, but you have to guess what the user would want.

Re: Algolia raises $110M for its search-as-a-service

#112

Post by nl »

mlthoughts2018 wrote: post #106
nl wrote: post #98

Earlier quoted context omitted.

I do both search and ML solutions in the area that Rekognition targets. In both cases they are great 80/20 solutions (actually Algoria is more like a 95/5 solution in most cases).

I also do computer vision and search in the areas targeted by Algolia and Rekognition and have not found this to be true at all. For face detection for example, Rekognition was completely unusable for my company.

I haven't used face detection by Rekognition so I can't comment.

However, I'm surprised it's that bad.

I've done two projects in the last 6 months that required face detection, and in both cases combinations of DLib and OpenCV performed perfectly well. Since these are entirely off-the-self models I don't see why Rekognition should - in principle - be any worse.

Re: Algolia raises $110M for its search-as-a-service

#113

Post by mlthoughts2018 »

nl wrote: post #112
mlthoughts2018 wrote: post #106

Earlier quoted context omitted.

I also do computer vision and search in the areas targeted by Algolia and Rekognition and have not found this to be true at all. For face detection for example, Rekognition was completely unusable for my company.

I haven't used face detection by Rekognition so I can't comment. However, I'm surprised it's that bad. I've done two projects in the last 6 months that required face detection, and in both cases combinations of DLib and OpenCV performed perfectly well. Since these are entirely off-the-self models I don't see why Rekognition should - in principle - be any worse.

Yeah, pre-built dlib and opencv models are similarly not realistic for real world applications. We ended up needing to train our own version of MTCNN and separately train celebrity face recognition.

Especially when detecting in images with many faces, these legacy off the shelf things built on Viola-Jones type models or HoG feature extractors are just not acceptable by comparison with deep learning models.

And even at that, you need to fine tune the model to your own specific dataset with appropriate weights to reflect asymmetry in false positives vs false negatives. Simply using any off the shelf model, even a deep CNN model, virtually never works in practice. Unless your real life task is well approximated by the academic data set used for training (and it never is), you’re going to need a computer vision engineer involved.

Re: Algolia raises $110M for its search-as-a-service

#114

Post by nl »

mlthoughts2018 wrote: post #113
nl wrote: post #112

Earlier quoted context omitted.

I haven't used face detection by Rekognition so I can't comment. However, I'm surprised it's that bad. I've done two projects in the last 6 months that required face detection, and in both cases combinations of DLib and OpenCV performed perfectly well. Since these are entirely off-the-self models I don't see why Rekognition should - in principle - be any worse.

Yeah, pre-built dlib and opencv models are similarly not realistic for real world applications. We ended up needing to train our own version of MTCNN and separately train celebrity face recognition. Especially when detecting in images with many faces, these legacy off the shelf things built on Viola-Jones type models or HoG feature extractors are just not acceptable by comparison with deep learning models. And even a…

You mentioned face detection earlier and now you are talking about face recognition. There's a huge difference.

For face detection DLib and OpenCV work really work in real world applications. As I mentioned I've deployed two real-world solutions using them in the past 6 months.

Re: Algolia raises $110M for its search-as-a-service

#115

Post by slashvar2701 »

mlthoughts2018 wrote: post #21
apalmer wrote: post #19

Earlier quoted context omitted.

You are absolutely factually correct in your analysis, but you are completely missing how business works. Fundamentally, there is value to most businesses in being able to just buy a decent solution to a non core competency. That’s where Algolia and AWS and basically all service companies come in... a medium scale clothing manufacturer with a booming e-commerce site may well know they have no clue how to do search, a…

I’m saying in my experience there is no such thing as one singular “decent solution” for search. It varies enormously from use case to use case, customer cohort to customer cohort, etc. To even know if you’re buying a decent solution from Algolia or not, you’d already have to hire pretty much all the same staff you’d have to hire to more cost-effectively build it in-house. I think the fundamental myth, just like with…

Disclaimer: I'm a software engineer working on the core search engine at algolia, but the opinions of this post are my own and not an official statement.

Search is a hard job, and it's hard in many ways. The most obvious difficulties are related to relevance, and yes this part is specific to each business case. But that's not the only issues one has to solve when implementing search.

Even without speaking about the software you run, running it so that you have high availability, fast search results, fast enough to provide search as you type, reliable indexing, low latency in several regions ... This is the first service we provided, this is what SaaS is about. Being on inside of a SaaS compagnie, shows you the amount of works we save to our customers.

Then, about software solution itself. Providing search is not just about running generic piece of code. It's a whole eco system, continuously evolving. Working with a SaaS solution is hiring a team of more than hundred engineers dedicated to search. From the core software to frontend UI modules, the amount of engineering needed is way above what most companies can dedicate to search.

Back to relevance, some aspects are specific to business logic but some are also specific to search. We provide the search knowledge, so that you can focus on your own issues.

And for the software behind our services, we're not trying to build the one size fits all search tool, but a tool dedicated to the kind of search needed in today's web applications. I'm obviously biased, but I strongly believe that the kind of search we focus on, fast accurate top results rather than exhaustive search, fits terribly well our users' needs.

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