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CRISP-DM on AWS

AWS Certified MLS | 05 Nov 2019
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CRISP-DM

Phases of CRISP-DM


Business Understanding

  1. Understanding business requirements
    • Questions from the business perspective which need answering
    • Highlight project’s critical features
    • People and resources required
  2. Analyzing supporting information
    • List required resources and assumptions
    • Analyze associated risks
    • Plan for contengencies
    • Compare costs and benefits
  3. Converting to a Data Mining or Machine Learning problem
    • Review machine learning question
    • Create technical data mining objective
    • Define the criteria for successful outcome of the project
  4. Preparing a preliminar plan
    • Number and duration of stages
    • Dependencies
    • Risks
    • Goals
    • Evaluation methods
    • Tools and techniques

Data Understanding

  1. Data collection
    • Detail Various sources and steps to extract data
    • Analyze data for additional requirements
    • Consider other data sources
  2. Data properties
    • Describe the data, amount of data used, and metadata properties
    • Fidn key features and relationshps in the data
    • Use tools and techniques to explore data properties
  3. Quality
    • Verifying attributes
    • Identifying missing data
    • Reveal inconsitencies
    • Report solution

AWS tools for Data Understanding


Data Preparation Tasks & Modeling

Data Prepation Tasks

1. Final Dataset Selection

2. Data Preparation

Data Modeling

1. Model selection and creation

2. Model testing plan

3. Parameter tuning/testing

AWS Tools for Data Preparation and Modeling


Evaluation

Reviewing the project

2. Make the final decision to deploy or not

Based on complete evaluation and business goals acceptance criteria we will take a decision wether a model will be deployed or not. This requires careful analysis of the false positives and true negatives.

Running Jupyter Notebook on EC2 Instance

  1. Create instance using Deep Learning AMI
  2. Connect to the instance using SSH
  3. Run: screen (read more …)
  4. Run Jupyter Notebook: jupyter notebook --no-browser OR jupyter notebook --no-browser --ip=0.0.0.0 --port=[choose your port]
  5. Copy-paste the URL containing the token to the browser and access the example

Deployment

1. Planning deployment

2. Maintenace and monitoring

Monitoring

3. Final report

4. Project review