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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…
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Designing Azure Data Science and AI Projects
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- 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." …
