Machine Learning as a Service

“Jack of all trades, master of none, though oft times better than master of one”

One of the common pain points that we have come across in organizations is the last-mile delivery of data science applications. There are two common delivery vehicles of data products – dashboards and APIs.

More often than not, machine learning practitioners find it hard to deploy their work in production and full stack developers find it hard to incorporate machine learning models in their pipeline. To be able to successfully do a data science-driven product/application, it requires one to have a basic understanding of machine learning, server-side programming and front-end application.

In this workshop, one would learn how to build a seamless end-to-end data driven application – Starting from data ingestion, data exploration, creating a simple machine learning model, exposing the output as a RESTful API and deploying the dashboard as a web application – to solve a business problem.

Course Content:

Data Engineering
1. Data Ingestion and Exploration

Machine Learning
2. A simple machine learning model

Dashboard
3. Dashboard using bokeh

API and Deployment
4. RESTful API
5. Integrating Model output to DB
6. Deployment

The repository for the workshop is here

Key takeaways

Learn how to build and deploy a machine learning application end-to-end