Completed
Deployment A model is not particularly useful unless the customer can access its results. The complexity of this phase varies widely, It has four tasks: 1. Plan deployment Develop and document a plan…
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Designing Azure Data Science and AI Projects
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 CRISP-DM - The Cross Industry Standard Process for Data Mining is a process model with six phases that naturally describes the data science life cycle. It's like a set of guardrails to help you plan …
- 3 Business Understanding Focus on understanding the objectives and requirements of the project. 1. Determine business objectivesYou should first thoroughly understand, from a business perspective, what…
- 4 Data Understanding It drives the focus to identify, collect, and analyze the data sets that can help you accomplish the project goals. It has four tasks
- 5 Data Preparation This phase, which is often referred to as "data munging", prepares the final data set(s) for modeling. It has five tasks: 1. Select data: Determine which data sets will be used and d…
- 6 Modeling Here you'll likely build and assess various models based on several different modeling techniques. It has four tasks
- 7 Evaluation The Evaluation phase looks more broadly at which model best meets the business and what to do next. It has three tasks: 1. Evaluate results: Do the models meet the business success criteri…
- 8 Deployment A model is not particularly useful unless the customer can access its results. The complexity of this phase varies widely, It has four tasks: 1. Plan deployment Develop and document a plan…
- 9 TDSP Microsoft's Team Data Science Process Launched in 2016, TDSP is an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently." …
