Saw an article this past week from AWS Re:Invent that they just released their Machine Learning curriculum and materials free to the public. Google (Cloud Platform and elsewhere) TensorFlow, (Facebook’s) PyTorch, and Microsoft Azure CNTK frameworks education is also available and has been for awhile now.
My money is on PyTorch and Tensorflow as being the two frameworks most likely to succeed. However all the above use many open source facilities and there seems to be a lot of cross breeding across them. Both AWS ML solutions and Microsoft CNTK offer PyTorch and TensorFlow frameworks/APIs as one option among many others.
AWS Machine Learning
I spent about an hour plus looking over the AWS SageMaker tutorial videos in the developer section of AWS machine learning curriculum. Signing up was fairly easy but I already had an AWS login. You also had to enroll/register for the course on your AWS login but once that was through, you could access courses.
In the comments on the AWS blog post there were a number of entries indicating broken links and other problems but I didn’t have any issues. Then again, I didn’t start at the beginning, only looked at over one series of courses, and was using the websites one week after they were announced at Re:Invent.
Amazon SageMaker is an overarching framework that can be used to perform machine learning on AWS, all the way from gathering, analyzing and modifying the dataset(s), to training the model, to creating a inference engine available as an endpoint that can be used to perform the inferencing.
Amazon also has special purpose API based tools that allow customers to embed intelligence (inferencing) directly into their application, without needing to perform the ML training. These include:
- Amazon Recognition which provides image (facial and other tagging) recognition services
- Amazon Polly which provides text to speech services in multilple languages, and
- Amazon Lex which provides speech recognition technology (used by Alexa) and together with Polly helps embed conversational interfaces into customer applications.
TensorFlow Machine Learning
In the past I looked over the TensorFlow tutorials and recently rechecked them out. I found them much easier to follow this time.
The Google IO 2018 video on TensorFlow, Getting Started With TensorFlow High Level APIs, takes you through a brief introduction to the Colab(oratory), a GCP solution that uses TensorFlow and how to use Tensorflow Keras, tf.data and TensorFlow Eager Execution to create machine learning models and perform machine learning.
Keras on TensorFlow seems to be the easiest approach to use machine learning technologies. The video spends most of the time discussing a Colab Keras code element, ~9 lines, that loads a image classification dataset, defines a 1 level (one standard layer and one output layer), trains it, validates it and uses it to perform inferencing.
Both Colab and AWS Sagemaker use and discuss Jupiter Notebooks. These appear to be an open source approach to documenting and creating a workflow and executing Python code automatically.
GCP Colab is essentially a GCP-Google Drive based Jupiter notebook execution engine. With Colab you create a Jupiter notebook on google drive and interactively execute it under Colab. You can download your Jupiter notebook files and essentially execute them anywhere else that supports TensorFlow (that supports TensorFlow v1.7 or above, with Keras API support).
In the video, the Google IO instructors (Josh Gordon and Lawrence Moroney) walk you through building a model to recognize handwritten digits and outputs a classification (0..9) of what the handwritten digit represents.
It uses a standard labeled handwriting to digits labeled data set, called the MNIST database of handwritten digits that’s already been broken up into a training set and a validation set. Josh calls this the “Hello World” of machine learning.
The instructor in the video walks you through the (Jupiter Notebook – Eager Execution-Keras) code that inputs the data set (line 2), builds a 1 level (really two layer, one neural net layer and one output layer) neural network model (lines 3-6), trains the model (line 7), tests/validates the model (line 8) and then uses it to perform an inference (line 9).
Josh spends a little time discussing neural networks and model optimizations and some of the other parameters used in the code above. He has a few visualizations of what this all means but for the most part, the code uses a simple way to build a neural net model and some standard optimization techniques for the network.
He then goes on to discuss tf.data which is an API that can be used to create machine learning datasets and provide this data to the neural net for training or inferencing. Apparently tf.data has a number of nifty features that allow you to take raw data and transform it into something that can be used to feed neural nets. For example, separating the data into batches, shuffling (randomizing) the batches of data, pre-fetching it so as to not starve the GPU matrix multipliers, etc.
Then it goes into how machine learning is different than regular coding. And show how TensorFlow Eager Execution is really just like Python execution. They go through another example (larger) of machine learning, this one distinguishes between cats and dogs. While they use an open source Python IDE , PyCharm, to test and walk through their TF Eager Execution code, setting breakpoints and examining data along the way.
At the end of the video they show a link to a Google crash course on TensorFlow machine learning and they refer to a book Deep Learning with Python by Francois Chollet. They also mention a browser version of TensorFlow which uses Java Script and your browser to develop, train and perform inferences using TensorFlow Keras machine learning.
Never got around to Microsoft’s Azure training other than previewing some websites but plan to look over that soon.
I would have to say that the Google IO session on using TensorFlow high level APIs was a lot more enjoyable (~40 minutes) than the AWS multiple tutorial videos (>>40 minutes) that I watched to learn about SageMaker.
Not a fair comparison as one was a Google IO intro session on TensorFlow high level APIs and the other was a series of actual training videos on Amazon SageMaker and the AWS services you can use to take advantage of it.
But the GCP session left me thinking I can handle learning more and using machine learning (via TensorFlow, Keras, Eager Execution, & tf.data) to actually do something while the SageMaker sessions left me thinking, how much AWS facilities and AWS infrastructure services, I would need to understand and use to ever get to actually developing a machine learning model.
I suppose one was more of an (AWS SageMaker) infrastructure tutorial and the other was more of an intro into machine learning using TensorFlow wherever you wanted to execute it.
I think I’m almost ready to start creating and feeding a TensorFlow model with my handwriting and seeing if it can properly interpret it into searchable text. If it can do that, I would be a happy camper
Screenshos from AWS Sagemaker series of tutorial video 1, 2, 3, 4 & 5, you may need a signin to view them
Screenshots from the Getting Started with TensorFlow High Level APIs YouTube video