Automating automation: a framework for developing and marketing deep learning models
Are you sold on the benefits of adding automation to your stack, but put off by the high entry barrier to this game? The NeoPulse Framework promises to ease the burden of developing Deep Learning models by introducing a number of interesting concepts.
Deep learning (DL), machine learning (ML) and artificial intelligence (AI) are getting a lot of traction and for good reason, as we have been exploring. According to recent surveys, it seems that by now most developers and executives have realized the importance of AI and are at least experimenting with it.
There is one problem though: AI is hard. AI sits on the far end of a spectrum of data-driven analytics applications. So if data science skills are so hard to come by, AI skills are even harder. DimensionalMechanics just announced its approach to closing the AI skills gap, called NeoPulse AI Studio (NAIS). NAIS is part of NeoPulse Framework (NPF), which combines a number of interesting approaches and may show the way for others to follow.
Let’s picture this simple scenario: since as we all know the internet is basically about cats, every organization would at some point want to develop an application to scan through images and figure out which ones contain cats for future use. That is actually an entry level classification task, something that DL excels at.
Entry level it may be, but easy it is not: you have to choose a DL framework to use,understand its inner workings and API, find or develop a model that is fit for the task, train it by feeding it sample data and then integrate it in your existing stack and deploy it. But then since at some point everyone would be able to do this, to stay one step ahead you’d have to also classify images of dogs. How would NPF help you there?