There are revolutionary changes happening in hardware and software that are democratizing machine learning (ML). Whether you’re new to ML or already an expert, Google Cloud Platform has a variety of tools for users. This session will start with the basics: using a pre-trained ML model with a single API call. It’ll then look at building and training custom models with TensorFlow and Cloud ML Engine, and will end with a demo of AutoML Vision – a new tool for training a custom image classification model without writing model code.

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19 thoughts on “Intro to machine learning on Google Cloud Platform (Google I/O '18)”

  1. I believe the example code for the wide model given at 29:48 is wrong. The model should be created like this:
    wide_model = Model(inputs=[bow_inputs, variety_inputs], outputs=predictions)

  2. How do you create ML for sifting through pubmed and analyzing medicine affects on specific genetic disorders? I know it would need 2 libraries, but how can one get it to connect missing dots overlooked by humans?

  3. this video doesn't make me want to use GCP for ML anytime soon. Most if not all what she talked about can be achieved quite easily in Jupyter notebook with opensource software (fast.ai etc)

  4. Great video, especially on AutoML :
    Is human labeling performed by real humans ? How do they know how to label, if not experts in the reviewed domain ?
    Is it possible to restrict the origin of API calls ? (Use case : don't want my competitors to use my prediction API) ?
    Is it possible to make predictions offline (something like compile model, then use it in JS in the browser) ?

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