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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  You might end up wading through tons of confusing, outdated information that will misguide you, causing you to go down rabbit holes that will make things seem unnecessarily complex.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...

I was graciously invited to give two panel discussions at the IoT World conference that happened last week in Santa Clara, CA.  Since the panels are not recorded, here are my thoughts and jots from before and during the Wednesday 5/15/2019 panel, entitled  Wrangling IoT Data for Machine Learning .  (Actually, I'm going into even more detail than I had time for at the panel.)  Despite that the conference organizers approached me about speaking on behalf of my former employer about some topics that honestly I was given just a few weeks to investigate and could only report back with failures even now, I managed to convince them that I was fluent in other things that were more generic -- unrelated to the job I knew I was about to quit. (Note: My thoughts and jots for the Thursday 5/16 panel are coming later.) Business Calculations The first question we were tasked with answering in this panel related to the business calculations that must be made before taki...

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...