INTRODUCTION TO AI PRODUCT MANAGEMENT

The Art and Science of Building Products That Leverage Artificial Intelligence

• Learn, from a PM currently working in the AI/ML domain,
• About how to leverage AI when building products,
• Without spending a ton of time taking classes, reading books, research papers and blogs or watching endless hours of YouTube videos!
• Distilled & Easy to Read Presentation!

If you’re a Product Manager and want to leverage AI or you’re a Data Scientist or Software Engineer who wants to move into Product Management, this presentation will help prepare you for interview questions, set you up for success with new projects or simply satisfy your curiosity about AI.

This presentation distills everything you need to know about leveraging AI in your products in an easy to understand language with limited technical jargon. It will show you how to go about creating roadmaps, working with your stakeholders and how to determine if AI is the right solution to solve the problem you’re trying to tackle. And last but not least, it will give you an overview of all the AI algorithms that you need to know about and the use cases that they are best suited to solve.

Topics covered in this presentation:

·         PRODUCT MANAGEMENT

·         UNDERSTANDING THE PROBLEM SPACE

·         IS AI THE RIGHT SOLUTION FOR THE PROBLEM YOU’RE TRYING TO SOLVE?

·         PRODUCT ROADMAPS

·         PRODUCT ROADMAP - BEST PRACTICES

·         AI PRODUCT – OBJECTIVES & KEY RESULTS

·         AI PRODUCT PHASES

·         AI PRODUCT – DISCOVERY & FEASIBILITY

·         KNOWLEDGE AND SKILLS REQUIRED TO BE A PRODUCT MANAGER IN AI

·         KEY COLLABORATION – PRODUCT MANAGER AND DATA SCIENTISTS

·         PRODUCT MANAGER’S UNIVERSE – KEY STAKEHOLDERS

·         STAKEHOLDER MANAGEMENT FOR AI PRODUCTS

·         STAKEHOLDER AI EDUCATION STRATEGY

·         STAKEHOLDER MANAGEMENT - BEST PRACTICES

·         WHAT IS ARTIFICIAL INTELLIGENCE?

·         MACHINE LEARNING ALGORITHMS

·         SUPERVISED vs. UNSUPERVISED LEARNING

·         SUPERVISED (KNN) vs. UNSUPERVISED ALGORITHMS (K-MEANS)

·         DATASET SPLITTING

·         SUPERVISED LEARNING ALGORITHMS

·         DATA LABELLING for SUPERVISED LEARNING

·         CLASSIFICATION ALGORITHMS

·         DECISION TREES, RANDOM FOREST & NAÏVE BAYES

·         LOGISITIC REGRESSION AND SUPPORT VECTOR MACHINES

·         APPROACHES TO TRAINING A CLASSIFICATION MODEL

·         LINEAR REGRESSION

·         DATA NORMALIZATION vs STANDARDIZATION

·         GRADIENT DESCENT

·         NATURAL LANGUAGE PROCESSING (NLP) & UNDERSTANDING (NLU)

·         USE CASES FOR NLP & NLU

·         NLP – KEY TERMS

·         NAMED ENTITY RECOGNITION (NER)

·         DOCUMENT SIMILARITY VIA TF-IDF

·         TRANSFORMER MODELS FOR NLU: BERT

·         TRANSFORMER MODELS FOR NLU: GPT-3

·         ARTIFICIAL NEURAL NETWORKS

·         WHAT IS A NEURAL NETWORK?

·         NEURAL NETWORK OVERVIEW

·         TYPES OF NEURAL NETWORKS

·         WHAT IS REINFORCEMENT LEARNING?