As part of the »PyCon.DE 2017 & PyData Karlsruhe conference«, visitors of the »Open Codes« exhibition can attend the workshop »Machine Learning as a Service« with Python-expert Anand Chitipothu. The workshop addresses one of the most common pain points we have come across with data scientists at many organizations: moving data science solutions to production.
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.
1.Introduction and Concepts
- Approach for building ML products
- Problem definition and dataset
- Build the ML Model
2. Build an ML Service
- Concept of ML Service
Deploy the ML Service - localhost API
3. Build a Dashboard
- Create a simple dashboard
- Integrate ML model API with dashboard
- Best practices and challenges in building ML service
- Best practices in deploying to Cloud
- Where to go from here