PRODUCT MANAGEMENT FOR AI & ML

• Instructor Introduction
• Course Objectives & Flow
• Q & A

Begin to explore the landscape of the ML/AI industry including

• The relevance, roles and responsibilities of an AI Product Manager
• How AI is currently used within the industry
• The differences between traditional algorithms vs AI vs Machine Learning

Assignment #1: Predict the trajectory that AI will take over the next five years in your organisation or an organisation of your choice.

Develop a hypothesis for a problem that can be resolved using ML/AI:

• Ideating for opportunities
• When to use AI and when not to
• Machine Learning domains
• Identifying a Machine Learning problem
• Workshop: Consider a set business scenario and create a hypothesis

Assignment #2: Create a hypothesis that is specific and actionable for a problem of your choice that you believe can be solved using ML/ AI.

Learn about the key aspects used to frame an ML/AI problem:

• Conducting market research
• Identifying target audience and user personas
• Assessing competition and potential market gaps
• Practical tools to structure your product thinking - The AI Project Canvas

Assignment #3: Use the AI Project Canvas as a tool to structure and convey the key value proposition for an ML/AI idea.

Understand the benefits of identifying the right features and setting boundaries for projects

• Working backwards framework for innovation: PR/ FAQ
• Hierarchy of needs within ML and AI
• Introduction to key Product Management metrics: MVP, MVD and MLP
• Managing trade offs - value vs complexity

Assignment #4: Prepare for the Capstone Project by familiarising yourself with the materials given about a real example for the PR/FAQ.

Understand the importance of data in successful AI projects

• Data Growth Strategy
• Open Source Data
• Organisational Data
• Annotating Data
• Acquisition & Purchase Data
• Organising Data
• Group Activity: Quiz

Assignment #5: Identify the most suitable data collection option for various problem statements.

Learn the fundamental processes required to build an AI solution with a real life case study

• Problem definition
• Experimental design
• Data preparation
• Exploratory data analysis
• Model selection & Model evaluation
• Model A/B testing
• Best practices for managing AI and ML development projects
• Case study: walk through a real AI program that uses deep learning to predict customer churn

Assignment #6: Describe how your ML system will turn predictions into value for end-users. You will be working with the ML Canvas - a specific framework designed to help you break down complex technical aspects into smaller, more digestible segments.

Become proficient in evaluating model performance, model metrics and selecting the optimum model for individual use cases

• Dividing Test Data
• The Confusion Matrix
• Precision, Recall and F1 score
• Optimisation for Experience
• Error Recovery

Assignment #7: X-cell biotech case study

Learn to recognise the critical role of experimentation in constructing AI/ML solutions

• Designing the AI/ML experiment
• Selecting the right models for experiments
• Communicating experiment data to your stakeholders
• Identifying and mitigating risks
• Scaling experiments

Uncover specific methods and models essential to Product Management in AI and ML to ensure that AI/ML solutions remain relevant, effective and user centric over time

• Model Deployment Methods
• Monitoring models
• Selecting a feedback metric
• User feedback loops
• Shadow deployments

Learn the ways in which AI and ML can optimise products and enhance customer satisfaction

• Identifying opportunities for product improvement and growth
• Using AI/ML for product optimisation and personalisation
• Scaling
• Measuring and improving customer satisfaction

Discover the importance of human factors within Product Management in AI and ML

• Human-centred design practices to inspire trust
• Ethical and privacy considerations
• Addressing bias and fairness in AI/ML algorithms
• Introduction to Explainable AI

Become familiar with the importance of communication and collaboration across both tech

• The importance of storytelling to articulate complex concepts in simple terms
• Communicating effectively with different experts from various backgrounds
• Building and leading cross-functional teams
• Encouraging a culture of innovation and continuous learning
• Guest Speaker