Big data is changing our world. Given the latest development in AI and Machine learning, there are huge potential we can leverage to transform the Enterprise Applications to be more intelligent and efficient.
At the same time, machine learning is not a magic pill for every issues we have. Like every innovation, we should ask what type of problems it can best address. In this session, we will explain differences in Machine Leaning algorithms, and tools to use.
Our team has been developing and leveraging machine learning products in real life customer environment in last 3 years. In the session, we will talk about our experiences in building up Machine Learning products, experiment, measure and adapt.
You will learn what are the types of machine learning project, product management and agile methods for the teams to improve.


Outline/Structure of the Case Study

  1. What is AI and Machine Learning (and what is not)? (5 mins)
    1. Supervised leaning
    2. Unsupervised learning
    3. Reinforcement learning
    4. What is (what is not) Machine Learning
  2. Begin with Problem: Type of the Machine Learning User Cases (10 mins)
    1. Understanding the Problem is absolutely critical
    2. Simple, Complicated, Complex, Chaos Problems
    3. Many Applications for Machine Learning
    4. Machine Problems: our experience in Finance, Customer Services and Human Resources
  3. Innovation Cycle for Machine Learning Product (5 mins)
    1. Experience sharing: how we get start, where we failed, how we succeed, what we have learning
  4. How to Apply Agile Product Management Approaches for Machine Learning (25 mins)
    1. Data is the New Oil, however not at same standard (Collection, Labeling, Cleaning), (5 mins)
    2. Experimental Approach for Machine Learning, Away from Deterministic Process (5 mins)
    3. Model Assessment: Measure the success of Machine Learning Product (5 mins)
    4. Agile Product Delivery: Deliver fast, better customer value (5 mins)
    5. Data bias and Ethics (5 mins)
  5. Wrap up discussion & questions

Learning Outcome

Understanding of Types of Machine Learning (what is and what is not)
Type of Problems and Related to Machine Learning
Innovation Cycle for Machine Learning Product
How to Apply Product Management Approach for Machine Learning

Target Audience

Innovation Team, Transformation, Agile Coaches, Scrum Master, Development lead, Executives

Prerequisites for Attendees

Basic understanding of AI and Machine Learning

Basic Understanding of Agile
Basic understanding of Product Management
schedule Submitted 1 year ago

Public Feedback

    • 480 Mins

      In this one-day Workshop, Dave Snowden, the creator of the Cynefin framework and famous in the agile community as an inspiring and sometimes controversial speaker, will address agility from the point of view of complexity. Participants will be exposed to a realistic approach that puts context before dogma and shows a future for Agile that goes beyond fighting over methods and towards a sophisticated application of agility in organizations.

      This class will offer an introduction to the Cynefin framework by its creator: the Cynefin framework is a transformational idea that uses a situation-specific approach to making sense of the world in order to act in it, and ensures effective work, decision making, and management even in complex and uncertain environments. For Agile practitioners, this framework supports effectively tailoring methods and practices to different situations. Cynefin-informed methods and practices help Agile organizations harness change and turn complex situations into a competitive advantage for customers and the business.

      DateTime: This workshop is scheduled on Oct 12th and 13th from 2 PM to 6 PM IST (4 hours each day)