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I was reading this article – https://medium.com/faun/mastering-the-mystical-art-of-model-deployment-c0cafe011175 which details how to use Amazon SageMaker for deployment of Machine Learning Models in a web production environment.

I wanted to know if there are any other tutorials about ML Model deployment using open source/other technologies.

https://iopscience.iop.org/article/10.1088/1742-5468/ab3985

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Abstract

The practical successes of deep neural networks have not been matched by theoretical progress that satisfyingly explains their behavior. In this work, we study the information bottleneck (IB) theory of deep learning, which makes three specific claims: first, that deep networks undergo two distinct phases consisting of an initial fitting phase and a subsequent compression phase; second, that the compression phase is causally related to the excellent generalization performance of deep networks; and third, that the compression phase occurs due to the diffusion-like behavior of stochastic gradient descent. Here we show that none of these claims hold true in the general case, and instead reflect assumptions made to compute a finite mutual information metric in deterministic networks. When computed using simple binning, we demonstrate through a combination of analytical results and simulation that the information plane trajectory observed in prior work is predominantly a function of the neural nonlinearity employed: double-sided saturating nonlinearities like yield a compression phase as neural activations enter the saturation regime, but linear activation functions and single-sided saturating nonlinearities like the widely used ReLU in fact do not. Moreover, we find that there is no evident causal connection between compression and generalization: networks that do not compress are still capable of generalization, and vice versa. Next, we show that the compression phase, when it exists, does not arise from stochasticity in training by demonstrating that we can replicate the IB findings using full batch gradient descent rather than stochastic gradient descent. Finally, we show that when an input domain consists of a subset of task-relevant and task-irrelevant information, hidden representations do compress the task-irrelevant information, although the overall information about the input may monotonically increase with training time, and that this compression happens concurrently with the fitting process rather than during a subsequent compression period.

Repo Link: https://github.com/cortexlabs/cortex

We’ve just released a new version of Cortex, our open source platform for deploying trained models from any framework as production APIs on AWS. With this newest version, Cortex now also supports:

• Auto Scaling. If your traffic increases, Cortex will spin up new replicas to handle things. If your traffic decreases, Cortex will reduce replicas to save on cost.
• Spot Instances. Cortex can run on AWS Spot Instances, which can reduce instance costs by as much as 90%.
• More Instance Types. Cortex now supports g3 and g4 instance types.
• Batched Predictions. Cortex can now batch predictions.

I have tried Unet, att_r2_unet and a lot of variations of 2d networks. All of them are fantastic. I wanted to try 3d unet but I failed, it didn’t do it is job at all. There are so many steps I probably missed up somewhere. There are so many new models nowadays, what has been proven to be the best out there for this job? I’m tired of trying new networks/methods randomly and then finding another network.

Hello everyone,

we are researchers from TU-Berlin and UL FRI, and we are doing a research on how people interact with certain explainable AI techniques. We are currently in the process of gathering data and we need people to take part in our survey. If you have 15-20 minutes to spare to participate that would be extremely helpfull. All the details about the survey are explained in the survey itself.

The survey is reachable at this link: https://dss.vicos.si/. It is meant to be solved on a computer. It was tested and is working on Google Chrome and Safari.

After we analyze the data we will obviously share the paper 🙂 Thank you in advance!

On a new image dataset, unedited, without adversarial noise injection, ResNeXt-50 and DenseNet-121 see their accuracies drop to under 3%. Other former SOTA approaches plummet likewise by unacceptable margins:

– Natural Adversarial Examples – original paper, July 2019

– These Images Fool Neural Networks – TwoMinutePapers clip, 5 mins

So who says it’s a scandal? Well, I do – and I’ve yet to hear an uproar over it. A simple yet disturbing interpretation of these results is – there are millions of images out there that we humans can identify with obviousness and ease, yet our best AI completely flunk.

Thoughts on this? I summarize some of mine below, along a few of authors’ findings.

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Where’d they get the images? The idea’s pretty simple: select a subset classified incorrectly by several top classifiers, and find alike images.

Why do the NN’s fail? Misclassified images tend to have a set of features in common, that can be systematically exploited –> adversarial attacks. Instead of artificially injecting such features, authors find images already containing them: “Networks may rely too heavily on texture and color cues, for instance misclassifying a dragonfly as a banana presumably due to a nearby yellow shovel” (pg. 4).

Implications for research: self-attention mechanisms, e.g. Squeeze-and-Excite, improve accuracy on ImageNet by ~1% – but on this new dataset, by 10%. Likewise, related methods for increased robustness may improve performance on benchmark datasets by a little, but by a lot on adversarial ones.

• Thus, instead of pooling all efforts into maximizing F1-score on dataset A, testing against engineered robustness metrics that’ll promise improvement on an unsampled dataset B may be more worthwhile (e.g. “mean corruption error” pg. 8).

Implications for business: you don’t want your bear-catching drone to tranquilize a kid with a teddy.

with all the advances in image recognition technology, are image based “type your distorted text” captchas relevant now ? what will be the future of captcha ?

I’d imagine this dataset would consist of nodes being each Wikipedia page and connections being web links between pages. The nodes could also have its text as a feature matrix?

Does this exist already?

I’m trying out this BERT model from TFHub.

If my input tokens are [CLS] Hello world ! [SEP] [PAD] [PAD] [PAD] [PAD]

Do I need to make a mask like [1 1 1 1 1 0 0 0 0] or should I do [1 1 1 1 1 1 1 1 1] and let the pad token take care of it?