HN user
Co-founder of FloydHub (https://www.floydhub.com/)
Can you share more about your hobby farm? I would love to learn more about how you got into that? My family had a small farm growing up and my parents are still actively working on the farm everyday and I would like to take that up at some point. So curious to hear what you farm and how much involved you are in the process.
Children at school use a screen for school work starting in middle school (and sometimes even in elementary school). It is very difficult for parents or teachers to always supervise this. I think the adults should educate the children of safe online behavior but like other real world experiences they need have the independence when being online too.
How does the camera on these new phones with mirrorless cameras at less than $1000 price point?
Dedicated cameras have around 20 Megapixels but much larger sensor size - but does it really matter if the most I would do is print them into a photobook?
StitchFix | Senior Data Platform Engineer | Remote (US) | Full Time | https://www.stitchfix.com/careers/jobs?gh_jid=4143150&gh_jid... At Stitch Fix, we’re about personal styling for everybody and we believe in both a service and a workplace where you can be your best, most authentic self. We’re the first fashion retailer to combine technology and data science with the human instinct of a Stylist to deliver a deeply personalized shopping experience.
The Platform team at Stitch Fix is a highly impactful group of engineers who develop some of the most mission critical infrastructure in the company. The team owns our API strategy, execution as well as systems and platforms to help unlock critical algorithmic capabilities. In addition we also invest in high-leverage, self-service platforms and tools to facilitate scalable research & development for our data scientists.
We are looking for engineers with strong experience in building out scalable distributed and production systems. You will be building new platform services, tools and infrastructure for delivering algorithmic models to production. You will collaborate and partner with different functions within Stitch Fix - Data Science, Product, Engineering and other platform teams.
If this sounds interesting, please learn more and apply at: https://www.stitchfix.com/careers/jobs?gh_jid=4143150&gh_jid... or reach out via email: naren.thiagarajan <at> stitchfix.com
StitchFix | Senior Data Platform Engineer | Remote (US) | Full Time | https://www.stitchfix.com/careers/jobs?gh_jid=4143150&gh_jid...
At Stitch Fix, we’re about personal styling for everybody and we believe in both a service and a workplace where you can be your best, most authentic self. We’re the first fashion retailer to combine technology and data science with the human instinct of a Stylist to deliver a deeply personalized shopping experience.
The Platform team at Stitch Fix is a highly impactful group of engineers who develop some of the most mission critical infrastructure in the company. The team owns our API strategy, execution as well as systems and platforms to help unlock critical algorithmic capabilities. In addition we also invest in high-leverage, self-service platforms and tools to facilitate scalable research & development for our data scientists.
We are looking for engineers with strong experience in building out scalable distributed and production systems. You will be building new platform services, tools and infrastructure for delivering algorithmic models to production. You will collaborate and partner with different functions within Stitch Fix - Data Science, Product, Engineering and other platform teams.
If this sounds interesting, please learn more and apply at: https://www.stitchfix.com/careers/jobs?gh_jid=4143150&gh_jid... or reach out via email: naren.thiagarajan <at> stitchfix.com
StitchFix | Senior Data Platform Engineer | Remote (US) | Full Time | https://www.stitchfix.com/careers/jobs?gh_jid=4143150&gh_jid...
At Stitch Fix, we’re about personal styling for everybody and we believe in both a service and a workplace where you can be your best, most authentic self. We’re the first fashion retailer to combine technology and data science with the human instinct of a Stylist to deliver a deeply personalized shopping experience.
The Platform team at Stitch Fix is a highly impactful group of engineers who develop some of the most mission critical infrastructure in the company. The team owns our API strategy, execution as well as systems and platforms to help unlock critical algorithmic capabilities. In addition we also invest in high-leverage, self-service platforms and tools to facilitate scalable research & development for our data scientists.
We are looking for engineers with strong experience in building out scalable distributed and production systems. You will be building new platform services, tools and infrastructure for delivering algorithmic models to production. You will collaborate and partner with different functions within Stitch Fix - Data Science, Product, Engineering and other platform teams.
If this sounds interesting, please learn more and apply at: https://www.stitchfix.com/careers/jobs?gh_jid=4143150&gh_jid... or reach out via email: naren.thiagarajan <at> stitchfix.com
I have a toddler at home. I purchased a kindle a few months ago and it has drastically increased my reading time. I try to carry the kindle around the house instead of my phone. And read whenever I can - 15 to 30 mins chunks. I also read in bed before going to sleep and the backlit kindle is great for that.
Also I have been renting kindle books from my library. Very easy to try books and continue reading only if I find it interesting!
I have been using CloudApp for similar use case. It’s pretty good for screenshot sharing with annotations.
Author here. We have been building ML infra for FloydHub for over 3 years now and learned a ton. It is not easy as we thought it was! We are open sourcing our learning in a blog series - hoping it will be useful for companies who build their own ML infrastructure.
This article focuses on how to use EC2 effectively and save overall cost for ML infra. There are a lot of low-hanging-fruit opportunities that most companies we work with don't adopt. Anything else I missed?
As the first deep-learning-enabled product to launch on Github.com, this feature required careful design to ensure that the infrastructure would generalize to future projects.
It is surprising to see that this is the first time DL is run in production at GitHub. GitHub has a large amount of fairly structured data in the form of code, issues, etc. Plus they have been dabbling with DL for more than two years [1].
It could be that the business problems that are critical to the growth of GitHub product may not need DL. Solving problems like best-first-issues, code search are useful to the end user but may not effectively grow the business metrics.
[1] https://github.blog/2018-09-18-towards-natural-language-sema...
This is a really good time to be a Independent Scientist (aka Gentleman scientist) in this field because how nascent deep learning and similar techniques are. It requires a lot of trial and error and time/cost investment to bring the AI techniques to the masses.
The FAANGs are trying to hire all the top talent (including Emil who wrote the post) but I believe these independent researchers will be the one finding new opportunities to make AI useful in the real world (like colorizing b&w photos, create website code from mockups).
The biggest challenge I see for these folks is the access to high quality data. There is a reason Google is releasing so many ML models in production compared to smaller companies. Bridging the data gap requires effort from the community to build high quality open source datasets for common applications.
I use VirtualPostMail for my business mail. It works very well and provides what you're looking for. But the mail address is not available in all cities.
Nice! As you go from 2D to 3D what are some unique issues you are expecting to run into?
I really enjoyed the article - great clarity and made a complex topic easy to understand.
I was pleasantly surprised that you are 11th grade! What other projects are you working on?
Great product! Are you talking to companies who might want to offer this as a benefit to their employees?
The article mentions that GPUs are on average 50-200 times faster for deep learning, I’m curious on how he came to that number. It has a lot to do with the code and the frameworks used. I haven’t come across a good comparison, most figures seems to be taken out of the blue.
At FloydHub, we use nvidia-docker in production for running DL jobs. It has been very solid for the past 6 months or so. We have also built a collection of open source DL docker images for various frameworks and we actively maintain them.
[1] https://hub.docker.com/r/floydhub/ [2]: https://github.com/floydhub/dockerfiles
Agreed, that is the target audience we are going after with FloydHub. If you have some time to chat about your startup, please send me an email naren[AT]floydhub.com. I would love to get your feedback and see what would make Floyd useful for companies like yours.
Sorry to hear that, we are constantly improving the docs. You can find the latest version here: http://docs.floydhub.com/
If something is not clear, contact us in the communication app on the website. We will be happy to assist you in any way possible.
Yes, we are planning to build a FloydHub marketplace for talented data scientists to find gigs. We believe Floydhub can showcase their work and expertise easily (similar to StackOverflow) and help find a suitable partners to work with. A wide range of industries like Medical, Oil and Finance have been collecting huge amounts of data and now with Deep Learning, they can effectively make use of that. So I definitely see a rise in demand for deep learning practitioners and we want to support them on Floydhub.
Deep Learning is a very open source friendly community - most of the popular frameworks/algorithms are in fact open source. In fact, Floydhub is built on top of a lot of open source projects. We definitely considered open sourcing the core components of Floyd but the Rethinkdb fiasco made us "rethink" that. Instead, we are supporting the open source community by hosting Deep Learning Docker images, popular datasets, and projects. In the future, we are planning to open source more parts of Floyd but not all (similar to Github).
Yes, our Enterprise offering allows for building more complex workflows. Everything (code, parameters, environment and data) is versioned for reproducibility. One of the big value-adds is efficient caching of parts of the pipeline to avoid repetitions and save resources. We have noticed that this usually results in 10x increase in the number of experiments run by teams.
I can definitely resonate with this. Deep learning is in such an early stage, the frameworks and tooling are still maturing and evolving rapidly. This makes it really hard to reproduce other's work. Maybe there will be one winner in the frameworks war (Tensorflow?) and things will be better.
Pipez sounds really useful, good luck!
YC definitely helps in giving us a lot of credibility when talking to customers especially enterprises. We have also been learning a ton about sales during the last couple of months - for a couple of engineer / data scientist it has been a humbling experience. Other than that there is not really any magic here!
The warning in the documentation page is no longer valid. We DO save the notebook files and keep them after the session is terminated. They will be part of the run output.
The docs have been updated to reflect this. Thanks for pointing this out.
Hi, Thanks a lot! We are always adding to our public datasets (with appropriate licences). So we would be very happy to set it up for you on Floyd. Agreed that uploading large datasets is not quick. Instead we recommend you download them directly in to Floyd. See: http://docs.floydhub.com/home/managing_output/ for an example.
Re: Kaggle, we haven’t had a chance yet but that sounds like a great idea.
Ah! Late night coding error :) It is fixed now. Thanks for pointing it out.
Floyd’s Infrastructure runs entirely on Docker. That makes it backend agnostic (our cloud offering currently runs on AWS). Floydhub uses nvidia-docker for the deep learning jobs that require GPU. We also version the entire pipeline (code, data, params and environment) for exact reproducibility.
GPUs instances are really expensive. One of the biggest challenges at the moment is around reducing this cost. Eg. Spot Instances and Spot Blocks. Still some challenges to be solved there.
We also want Floyd to be an end-to-end solution for building, training and deploying deep learning models. In that vein, we are also investing in adding support for Tensorflow serving but it has been a rough ride so far. Getting a generic solution that can host any Tensorflow model has not been straightforward.
Hi! I'm Naren, the other co-founder of FloydHub. I'll be happy to answer any questions and really appreciate any feedback you can provide. Thanks!
Congrats on the launch Ryan! Upkeep looks great.
Did your previous employer know that you were working on this for such a long time? How did you pull it off?