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How does one go about deploying a model on Amazon SageMaker from Keras, TensorFlow, or TensorFlow Hub without first doing training? There are countless articles and blog posts discussing how to train a machine learning model using TensorFlow or Keras, and then deploy that model right away to Amazon SageMaker.  But what if you're already starting from a SavedModel, or just want to serve up a model trained on plain vanilla ImageNet from within your own AWS account?  You might end up wading through tons of confusing, outdated information that will misguide you, causing you to go down rabbit holes that will make things seem unnecessarily complex.  For instance, you might be inclined to use compatibility libraries to find low-level attributes of modern classes to leverage older deprecated function calls, or to build totally unnecessary infrastructure along the side that makes a Docker container for your model, which will be inevitably broken because you don't know how to invok...

Ever thought it would be cool to make an Android app that fuses Augmented Reality and Artificial Intelligence to draw 3D objects on-screen that interact with particular recognized physical objects viewed on-camera?  Well, here's something to help you get started with just that! Making conference talks can be a chicken-and-egg problem.  Do you hope the projects you've already worked on are interesting enough to draw an audience, or do you go out on a limb, pitch a wild idea, and hope you can develop it between the close of the call for papers and the conference?  Well, in this case, the work I did for DevFests in Chicago and Dallas yield a template for talks formulated by either approach. The most impressive part is that you can recreate for yourself  the foundation I've laid out on GitHub  by cloning the Tensorflow Git project, adding Sceneform, and editing (mostly removing) code.  However, it wasn't such a walk in the park to produce.  Here are th...

In Francois Chollet’s book Deep Learning with Python , he stresses the importance of utilizing a separate validation set of data while training a machine learning model in order to test periodically (say after every epoch) that the accuracy on something else besides strictly the training data (e.g. this validation set) is in fact improving. Machine learning models are subject to learn relationships that have nothing to do with the problem at hand.   For instance, a model tasked with trying to determine which way a military tank is facing might end up making assumptions based on whether it is day or night.   This is often a result of trying to eke out the model’s maximum performance, say by optimizing for the smallest value of a loss function.   However, what ends up happening is that the model overfits on the training data, which means it loses its generalization — its ability to predict the correct outcome of new samples or examples that we as humans would inten...

Among all the hype around the latest and greatest technologies, there is so much publicity devoted toward how they are being used in grand schemes to cure cancer, reduce energy waste, conserve water, solve poverty, and so forth.  While all these things are wonderful to humanity, there has to be someone left in the background who helps all the do-gooders unwind when it's time to take a break! The TL/DR Version: Get To the Point! Use clever arguments when loading up your Docker container so you don't have to shut it down and restart it when you want to mount external directories from the host filesystem or expose the port for the Tensorboard server.  There is also nvidia-docker available if you want to use your CUDA cores. sudo nvidia-docker run -it -p 6006:6006 -v ~/Pictures/video-game-training/:/video-game-training gcr.io/tensorflow/tensorflow:latest-devel-gpu bash Use the  --output_user_root  option in your Bazel builds so you can save it to that external ...