Algolia vs Amazon Machine Learning: What are the differences?
Algolia: Developer-friendly API and complete set of tools for building search. Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard; Amazon Machine Learning: Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology. This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
Algolia can be classified as a tool in the "Search as a Service" category, while Amazon Machine Learning is grouped under "Machine Learning as a Service".
Some of the features offered by Algolia are:
- Database search
- Search as you type
On the other hand, Amazon Machine Learning provides the following key features:
- Easily Create Machine Learning Models
- From Models to Predictions in Seconds
- Scalable, High Performance Prediction Generation Service
Medium, StackShare, and Product Hunt are some of the popular companies that use Algolia, whereas Amazon Machine Learning is used by Apli, Cymatic Security, and FetchyFox. Algolia has a broader approval, being mentioned in 258 company stacks & 54 developers stacks; compared to Amazon Machine Learning, which is listed in 9 company stacks and 10 developer stacks.
What is Algolia?
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Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.
Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.
Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.
Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.
Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.
Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.
Maxime is a big supporter of Product Hunt, recognizing the continual request that I add search to the product from others in the community. Having seen many frustrating search implementations on other sites, I assumed it would be hard to integrate and provide something useful. Algolia proved me wrong (see the results here: http://producthunt.co/search).
I'm impressed with the speed and amazing support from the Algolia team. The dashboard analytics and management are incredibly useful, providing insight into how people are using the product and ability to act on those learning without changing a line of code. I would highly recommend it.
Having a great search engine is extremely important for our app store. We find that users love to search, not only when they know what they are looking for but also to discover content around different themes.
In a very rushed period with lots of things to do in parallel, we found that Algolia offered a high quality solution that perfectly solved our problem and we had a first version working great in less than a day.
We also enjoyed getting their feedbacks and ideas to help us improve our search and we are now using Algolia in our internal tools as well. We strongly recommend them!
We were looking for a better search solution for GrowthHackers.com for a couple months. All the options we looked at were either too complicated to setup, didn't have the features we needed, or were too expensive. Algolia hit the right balance for us. It's super fast and easy to customize, and the documentation and examples for getting started are great. Most importantly though, their support rocks. It's always a pleasure talking with their team.
I'm Antonio, TVShow Time's CEO, a startup that has more than 100k+ active users.
Before Algolia, we were using Elastic Search that was costing a lot (hosted on 2 big EC2 instances) and with results that weren't that relevant.
Then we switched to Algolia, in 1 hour. We were blown away by how easy the integration was for such a good relevance in results and high performance.
We tried a lot of services at Socialcam to handle our massive user base. All of them couldn't handle that number of users.
Algolia handles it without any problem but on top of that, it does it at full speed: we get results back in under a few milliseconds. Last but not least, it does it with error handling, which is great as typos are very frequent on mobile...
Algolia helps us search across disparate pieces of information in our staff portal, and allows customers to easily jump around the portal between devices, support conversations, and documentation.
This is the bedrock of kako.pk - it not only serves the JSON data, it doubles as a (very fast) web-server if you connect to the client JS widget libraries
We use algolia to power our product search / filtering (https://www.shoesofprey.com/shoes).
We started with Algolia, but switched to our home-backed full-text search solution. It's serverless, based on Lunr.js