The Team Data Science Process With Python

data-science-process-python

I’m in Las Vegas this week for Ready and Inspire, Microsoft’s internal / external conference, and have been teaching colleagues how to create their own ML / DL models and deploy them to the cloud for customers to consume.

The AI team at Microsoft has also done a great job of putting together resources for others to learn from, and I wanted to share a workshop I found to be extremely helpful. You can find the link to it here.

From the Microsoft Cloud and AI Team

  • To use this notebook from this location, Sign in, and then click the Clone button above to add it in to your own (Free) Notebook Server. The “Student Notebook” is for the students to use, and does not contain the answers to the labs. The “Instructor Notebook” is for the teacher to use with the RISE presentation tool.
  • New to Azure Notebooks? More here: https://notebooks.azure.com/help/libraries
  • The Microsoft AI Landscape is here

The Team Data Science Process (TDSP) is an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently. TDSP helps improve team collaboration and learning. It contains a distillation of the best practices and structures from Microsoft and others in the industry that facilitate the successful implementation of data science initiatives. The goal is to help companies fully realize the benefits of their analytics program.

The TDSP is comprised of the following key components:

  • A data science lifecycle definition
  • A standardized project structure Infrastructure and resources for data science projects Tools and utilities for project execution

Note: You can follow a complete example of this process using Azure Machine Learning:

This workshop guides you through a series of exercises you can use to learn to implement the TDSP in your Data Science project, using only Python in a Notebook. You can change the Setup and Lab cells in this Notebook to use another language, another platform, and with more or fewer prompts based on your audience’s needs.

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