Building a ML Classification application with Gramex

Table of Contents

1. Workshop steps
2. For organizers
3. For participants

Workshop steps

Step 1 - Setup and Installation

To install gramex and its dependencies, please see the installation guide.

Step 2 - Overview

• Structure of workshop in structure.md.
• Workshop walkthrough instructions in snippets/ directory.

Workshop Outline

The workshop content is prepared for 1.5 hrs.

Section	Content	Duration
Introduction	Gramex intro, controlling data with URL params	20 mins
Build an ML app	Use snippets and build the application step-by-step	50 mins
Q&A, Support	Q&A session, help the participants	10 mins
Possibilities	Other ML applications, Email alerts, Charts, Screenshots, Admin module	10 mins

For workshop organizers

Please review the instructions on how this workshop content is put together at demo_setup.md

What to say

• How Gramex can help build custom applications with interactions, and charts
• How to use Gramex to build ML as a service
• Continue with content in structure.md

In the workshop, participants will learn how to create API endpoints for data and ML services.

Run locally or on cloud

• To set up and use this application locally, see the demo_setup.md file.
• To set up on cloud, read install.md file.
• To learn how this app was built, see the tutorial.

For workshop participants

Audience and prerequisites

The workshop is intended for developers who are new to Gramex.

Prerequisites

• Participants are expected to be comfortable with programming (intro-level HTML, JavaScript, Python).

Objective of the workshop

Participants will get familiar with few Gramex components: back-end configuration via YAML, writing front-end (JavaScript) code.

At the end of the workshop

Participants will get familiar with different utilities in Gramex to

• handle data (using FormHandler)
• run arbitrary functions (using FunctionHandler)
• customize components (using UIComponents)
  • add a utility to upload files (using UploadHandler)
  • add an interactive table (using g1 FormHandler)
  • productionize machine learning models
    • add endpoints to control machine learning classification algorithms in the UI
    • in the backend, explore how to use scikit-learn on-the-fly

View the live version of the application at https://9018.gramex.gramener.co/

Contributors

• Jaidev Deshpande jaidev.deshpande@gramener.com
• Nivedita Deshmukh nivedita.deshmukh@gramener.com
• Kamlesh Jaiswal kamlesh.jaiswal@gramener.com
• Bhanu Kamapantula bhanu.kamapantula@gramener.com