A Life Cycle view of ML: Addressing Cardinal Issues

Mr Tarun Bonu1, Mrs Komathy Padmanabhan1, Mr Gnana Bharathy2

1Monash University, Clayton, Australia, 2ARDC, Melbourne, Australia

In order to explore key issues in AI/ML, we propose to stage a Workshop to start with a short presentation/ roundtable discussion on some of the most pressing issues in AI/ ML and then open the floor for a discussion.  We aim to explore the key practices, challenges, limitations, and workarounds, relationships between various ideas grounded in practitioner experience.

A data science life cycle defines the stages or steps in a machine learning or data science project. We will use this life cycle view to classify key activities. The workshop will be split along these life cycle stages. For each stage, a mini-workshop will be conducted. Each workshop will have a presentation followed by a discussion relating to example use cases in that particular life cycle.

Note: While there are multiple life cycle depictions such as CRISP-DM, Team Life Cycle, Domino etc, in the following, we have adopted the common stages.

  1. Business Understanding and Framing the Problem
  2. Data Acquisition, Pre-Processing, and Exploration
  3. Feature Engineering, Model Development and Validation
  4. PoC, Customer Acceptance, PoC to Production

Deployment, Monitoring

  1. Pan-Cycle Issues: Explanation, AI Risk, Ethics, Participation, Value Creation and Measurement

Who should attend: AI/ML Practitioners. Researchers, Data scientists, Managers, Student Researchers

Skills or knowledge attendees should have: Some understanding of Machine learning will be useful

Do attendees need to install any special software to participate in the workshop: Participants will need to have access to Zoom with Audio and Visuals enabled

Pre workshop preparation: NA


Oct 18 2021


12:00 pm - 4:00 pm

Local Time

  • Timezone: America/New_York
  • Date: Oct 17 - 18 2021
  • Time: 9:00 pm - 1:00 am