Currently the SageMaker only supports training new models with Apache MXNext, I have had a little experience training model using Tensor Flow and was hoping to import my existing models. It was a good exercise for me looking at how MXNet works and expand my scope of ML frameworks. I look forward to the addition of future ML Frameworks to the DeepLens platform so I can continue trying them out to find the best options for the job.
Accomplishments that I'm proud of
Even though I was able to quickly get up and running using the sample models, I was happy I took the extra time building out my own model using SageMaker and Jupyter notebooks. Python is not my strongest language and working with Jupyter notebooks made it a little easier to work through the process. I recommend after you get a couple of the sample models up and running you take the time to train your own model as that really extends the value of this platform.
What I learned
It was interesting to explore the Apache MXNet framework, I had previously used Tensor Flow and I like to make sure I am keeping on top of multiple options when considering technologies. Apache MXNet is a very mature project and active user community.
I learned how this customized hardware and software integration allows for very fast deployment of deep learning methodologies and a quick learning tool for developers to begin exploring the technical area.
I was also very happy to find the extensive documentation that was put together by the AWS DeepLens team. It was a very well documented step by step that was helpful when digging into a totally new platform.
This first pass of my project is great for weeding out the birds from the squirrels, but I am interested in extending the model to do more detailed identifications. The images captured during the first phase of the project will be a good resource as I extend the training to more specifically identify bird species.
As I mentioned above, currently the SageMaker platform only supports MXNet models, I am interested in porting over my Tensor Flow trained models. Here is a link to my git project with my training information for bird species identification using Tensor Flow.
I am also very interested in seeing if I can adapt the hardware to include bird songs as part of the identification criteria.