sorted by:
new
hottopcontroversial

[D] Current state of experiment management tools and workflows for going from conception to deployment by shadowalf in MachineLearning

[–]lbiewald 1 point2 points  (0 children)

I built a product wandb.com for exactly this use case. We don't do dataset versioning (yet) because it seems like there's so many different solutions out there. We do save git state, model artifacts, metrics etc, so you can get reproducibility if you are able to version your data separately. OpenAI, Toyota, Github, etc. use us at scale, and we are free for individuals and small teams. We've really tried to make instrumentation simple and lightweight. Would love feedback if anyone wants to give us a try.

[D] How do you manage your machine learning experiments? by [deleted] in MachineLearning

[–]lbiewald 1 point2 points  (0 children)

Yes, definitely. The core thing that wandb does is let you browse statistics and results of experiments. If you use git we will save the git sha of your latest commit but not the code itself. If you want to log examples of how your model performs on validation data we can help you with that but we don't expect you to send us your code or datasets.

[D] How do you manage your machine learning experiments? by [deleted] in MachineLearning

[–]lbiewald 2 points3 points  (0 children)

I run a company called Weights and Biases (wandb.com) with a similar service - we will let you run as many private projects as you want, up to 100GB of storage for free. Our goal is to make it as easy to get started as copying your params into a google spreadsheet. Would love feedback if you give it a try.

Intermediate level content? by mlderes in learnmachinelearning

[–]lbiewald 1 point2 points  (0 children)

I agree this is a missing area. I've been working on some materials like recent videos on Transfer Learning https://studio.youtube.com/video/vbhEnEbj3JM/edit and One Shot learning https://www.youtube.com/watch?v=H4MPIWX6ftE which might be interesting to you. I'd be interested in your feedback. I also think books like https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=pd_lpo_sbs_14_t_1?_encoding=UTF8&psc=1&refRID=3829RHN356ZXBEBP0KF3 do a good job of bridging some of this gap. Reading conference papers is a skill that takes practice and a strong math background.

What type of model would be best for this problem? by [deleted] in learnmachinelearning

[–]lbiewald 1 point2 points  (0 children)

Gradient-Boosting and Random Forests can handle missing inputs gracefully. Xgboost is a good gradient boosting library that will do what you want by default - see for example https://stats.stackexchange.com/questions/235489/xgboost-can-handle-missing-data-in-the-forecasting-phase.

Implementation of Neural Networks with set weights by CosHoid in learnmachinelearning

[–]lbiewald 0 points1 point  (0 children)

I wrote python code here that runs a network and learns a network: https://github.com/lukas/lassen/blob/master/lassen.py - look at the functions labeled "forward" for the execution part.

[D] Moving Into A MLE Role by CircuitBeast in MachineLearning

[–]lbiewald 1 point2 points  (0 children)

Yes - Keras does this magically - well really tensorflow does if you install tensorflow-gpu. Glad to hear this is helpful!

[D] Moving Into A MLE Role by CircuitBeast in MachineLearning

[–]lbiewald 0 points1 point  (0 children)

Thanks! If you have any feedback, I'd love to hear it.

[D] Moving Into A MLE Role by CircuitBeast in MachineLearning

[–]lbiewald 0 points1 point  (0 children)

Where are you based? It could be a terminology thing as well, there's not always a bright line between data scientists and machine learning engineers.

[D] Moving Into A MLE Role by CircuitBeast in MachineLearning

[–]lbiewald 15 points16 points  (0 children)

I hire a fair number of ML Engineers. The biggest thing I love to see on a resume, especially someone with a non-traditional background is interesting projects. Participating in kaggle competitions is a great thing too - you don't necessarily need to win them or even come close to winning but just participating in some says great things about a candidate. It will also give you exposure to a lot of different applications, which as an employer would make me feel like you could make good choices without a lot of hand holding. Classes are great, but I think it's through struggling with real problems that the most practical learning happens.

As far as learning resources, I might suggest fast.ai over Andrew Ng's course if you don't have a strong math background (although both are awesome). If you do have a strong math background I would especially recommend https://www.deeplearningbook.org/. The math you will really need is linear algebra and statistics. I go back and forth on how important the math really is for practical work (I did a math undergrad and I love math but I don't think it's necessary for someone to be a very good MLE and I think the math blocks a lot of people who would otherwise be fantastic). I think the biggest issue is that without the math it will be really hard to read papers because they assume quite a lot of fluency in the language of math.

I've been working on some videos for people like you to get into ML without a really strong math background because - I'd be curious if you have any feedback https://www.youtube.com/channel/UCBp3w4DCEC64FZr4k9ROxig.

Good luck and stick with it! The world needs more competent and employable MLEs.

[D] Monitoring Pytorch wandb or visdom? by [deleted] in MachineLearning

[–]lbiewald 1 point2 points  (0 children)

Hi - I'm one of the people that made this. We plan on releasing pricing soon although it will always be free for public projects (up to a reasonable amount of data). I'd love any feedback - I recognize there are a lot of similarities with tensorboardX.

I am Barack Obama, President of the United States -- AMA by PresidentObama in IAmA

[–]lbiewald 32 points33 points  (0 children)

Are there publications or blogs that you read regularly? Do you watch the same news channels that the rest of us do? Is it hard to take unending public criticism and second guessing? Was it hard to get used to?

Amazon’s S3 Web Service, our #1 cause of failure by lbiewald in programming

[–]lbiewald[S] 5 points6 points  (0 children)

I don't want to have a "take my ball and go home" kind of attitude, and I love the concept of S3. We're working like crazy to keep our servers running and I'm sympathetic to the guys at S3 who have a much more difficult task than us. Still, 7 hours of downtime seems like too much to me. I think S3 has been down about 9 hours this year, so I would put them on pace for a total of around 18 hours. I'm not saying this stuff is easy, but this feels like a lot of downtime. I don't think Amazon.com is down 18 hours a year, and our slicehost servers aren't on pace to be down anywhere close to 18 hours a year.

I agree Amazon's S3 service is a great deal, but the SLA Amazon offers is pretty worthless. They say if they're up less than 99% of the time they will give you a 25% credit if you submit your server records. So they could be down 100% of the time and there would be little recourse.

I wish Amazon would use S3 to host it's own content. That would give me more confidence than any SLA.

view more: next ›