ML app design

Build and Deploy Data Science Products : A Practical Guide to Building a Machine Translation Application

This journey is going to be a 8 steps.

In this series we will take a use case, understand the solution landscape and its evolution, explore different architecture choices, look under the hood of the architecture to understand the nuts and bolts, build a prototype, convert the prototype into production ready code, build an application from the production ready code and finally understand the process for deploying the application .

1. Understand the landscape of solutions available for machine translation

2. Explore sequence to sequence model architecture for machine translation

3. Deep dive into the LSTM model with worked out numerical example

4. Understand the back propagation algorithm for a LSTM model worked out with a numerical example

5. Build a prototype of the machine translation model using a Google colab / Jupyter notebook

6. Build the production grade code for the training module using Python scripts

7. Building the Machine Translation application -From Prototype to Production : Inference process

8. Build the machine translation application using Flask and understand the process to deploy the application on Heroku