Can RPA help data scientists build deep learning model and predict from data?

The answer is YES. The Uber AI Labs team has open sourced Ludwig (A New Toolbox for Training Deep Learning Models Without Writing Any Code) that enable many of the fundamental building blocks of deep learning solutions.

Ludwig can use a convolutional neural network(CNN) as an encoder and a recurrent neural network(RNN) as a decoder. Those decisions are based on the characteristics of the data and require minimum input from the data scientists. (Refer the below diagram)

Problem Statement:
Training is one of the most developer intensive aspects of deep learning applications. Typically, data scientists spend numerous hours experimenting with different deep learning models to better perform about a specific training data sets. This process involves more than just training including several other aspects such as model comparison, evaluation, workload distribution and many others. Given its highly technical nature, the training of deep learning models is an activity typically constrained to data scientists and machine learning experts and includes a significant volume of code. While this problem can be generalized for any machine learning solution it has gotten way worse in deep learning architectures as they typically involve many layers and levels.

Scope of RPA:
Simplifying the training processes is the number one factor that can streamline the experimentation phase in deep learning solutions. Interestingly, Ludwig comes with a Python API which can be consumed by any RPA tool and the input and output parameters can be passed as arguments to create a desired python file and obtain the model. Considering the test data is accessible by the robots, RPA can be an apt solution to help data scientists by giving a final architecture by simultaneously predicting the output and evaluating the results for enormous volume of data.

Proven solutions for RPA & Python API integration can be seen here. (Videos)

By,

Vishal

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