For deploying models into production we need : data, a trained model, API’s around the model, nice UI/UX experience (for services from the browser), good infrastructure, best coding practices etc.
There are 4 main categories in a deep learning project before production.
The better way would be to allocate equal time for each task.
Underestimating the constraints and overestimating the capabilities of deep learning may lead to frustratingly poor results. So be keen on understanding what is needed.
Conversely, overestimating the constraints and underestimating the capabilities of deep learning may mean you do not attempt a solvable problem because you talk yourself out of it. So don’t stop yourself from trying a model. Iterate your learnings.
It’s better to iterate the project end-to-end rather than just fine-tuning the model or making some fancy GUI.
It’s only by practicing (and failing) a lot that you will get an intuition of how to train a model.
Start learning with the existing examples and the existing domains where deep learning is already applied and then look for more branches.
There are many accurate models that are of no use to anyone, and many inaccurate models that are highly useful.
A Drivetrain approach of how to use data not just to generate data but to produce actionable results is shown in the below picture:
Screen Shot 2021-06-30 at 4 53 10 PM
Below is the cool little gist which shows right from how to make our datasets to training & inference. https://gist.github.com/RaviChandraVeeramachaneni/12b2ed5ef7342048f92a86b019d4fd2f
Some of the problems to understand while building data centric products with deep learning involved:
Understanding and testing the behavior of a deep learning model is much more difficult than with most other code we write.
The neural network’s behavior emerges from the model’s attempt to match the training data, rather than being exactly defined. So this could be a disaster.
Out-of-domain data and domain shifts are another problem to be considered.
One possible approach outlined to understand the problems would be best described by below Image.