Designing & Building A Successful Ai Products & Solutions

AI Products, AI Companies

What you'll learn

User Centric Design of AI

AI Adoption & Usage

Risks of AI & Responsible AI Approach

User Expectation Management from AI Applications

Aligning AI with evolving User Expectations

Aligning AI with evolving User Expectations

Requirements

No pramming background required. A basic business fundamentals would be preferred

Description

The AI Product Management course from Project Tailwind explores AI’s place in the Product Development Lifecycle, key technical and AI concepts, decision frameworks and best practices, and the product mindset and human-centered design approach required to develop useful AI products. Our goal is to empower product managers to accelerate AI adoption and bridge the gap between business requirements and AI capabilities.WHO IS THIS COURSE FOR? This course is designed for those seeking to comprehend the AI product lifecycle, AI Product Management, and the process of launching AI features—whether currently integrating AI into their products and services or planning to do so. Whether you are looking to optimize product performance using AI or aiming to level up your Product Management career with AI, this course provides comprehensive training and practical insights from top industry professionals. The course is suitable for: Early to Mid-career Product Managers Business Solutions Architects Technical Project Managers Senior Business ExecutivesCOURSE DETAILSModule 1: AI FundamentalsModule 2: AI DiscoveryModule 3: AI DesignModule 4: AI Prototyping or Proof ofConceptModule 5: AI Develop to ScaleModule 6: AI DeliveryModule 7: AI OptimizationModule 8: Capstone Project,Learning ApproachConcept Explanation: 20%Watch + Learn: 60%Do + Learn: 20%

Overview

Section 1: Discovery for AI

Lecture 1 Introduction

Lecture 2 Theory of Ideation - Definition

Lecture 3 Theory of Ideation: The Process

Lecture 4 Theory of Ideation: Types

Lecture 5 Step 1: User or Stakeholders Needs

Lecture 6 User Research Methods

Lecture 7 Watch + Learn: Stakeholders Analysis

Lecture 8 Step 2: AI Opportunity Analysis - AI Triangle

Lecture 9 AI Triangle: Automation vs. Augmentation

Lecture 10 AI Triangle: AI Capabilities & Strengths

Lecture 11 Step 3: Framing a Big Ideas or Vision

Lecture 12 Summary & Exercises

Lecture 13 Watch + Learn: Big Ideas

Lecture 14 Do + Learn: Assignment Activities

Section 2: Design for AI

Lecture 15 Introduction

Lecture 16 Mapping User Needs with AI Capabilities

Lecture 17 Data Collection Guidelines

Lecture 18 Watch + Learn: Identifying Data Needs

Lecture 19 Definiting Objective function for AI

Lecture 20 Watch + Learn: Objective Function

Lecture 21 Build vs. Buy

Lecture 22 Prototyping & Validation

Lecture 23 Types of AI Prototyping

Lecture 24 Watch + Learn: AI Prototyping

Lecture 25 How to decide AI Prototyping

Lecture 26 Watch + Learn: AI Prototyping Principles

Lecture 27 Summary & Exercises

Section 3: AI Development

Lecture 28 Module Introduction

Lecture 29 Overview of ML life-cycle

Lecture 30 Machine Learning Life cycle

Lecture 31 Team structure

Lecture 32 Model Building: 1. Translate business requirements

Lecture 33 Do + Learn: Assignment Activities

Lecture 34 Watch + Learn: Translate business requirements

Lecture 35 Model Building: 2. Data Collection & Preparation

Lecture 36 Data Collection & Preparation

Lecture 37 Watch + Learn: Data Collection & Preparation

Lecture 38 Model Building: 3. Exploratory Data Analysis

Lecture 39 Watch + Learn: Exploratory Data Analysis

Lecture 40 Model Building: 4. Experimentation & validation

Lecture 41 Watch + Learn: Model Experimentation

Lecture 42 Do + Learn: Assignment Activities

Lecture 43 Model Deployment: Introduction

Lecture 44 Business Embedding

Lecture 45 Watch + Learn: Business Embedding

Lecture 46 Model Deployment Strategies

Lecture 47 Model Deployment Performances

Lecture 48 Model Deployment Testing Patterns

Lecture 49 Model Deployment Strategies Example 1

Lecture 50 Model Deployment Strategies Example 2

Lecture 51 Watch + Learn: Model Deployment Strategies

Lecture 52 Module Summary

Section 4: Delivery of AI

Lecture 53 Module Introduction

Lecture 54 Calibrating User Trust

Lecture 55 Provide First-hand Information

Lecture 56 Account for User Expectations

Lecture 57 Balance Control vs. Automation

Lecture 58 User Onboarding

Lecture 59 Step 1: Framing a User Mental Model

Lecture 60 Step 2: Setting User Expectations

Lecture 61 Step 3a: User Explaination - Be Transparent

Lecture 62 Step 3b. User Explaination - Optimize for User Understanding

Lecture 63 Step 3c. User Explaination - Manage Influence

Lecture 64 Manage AI Errors & Graceful Failures

Lecture 65 Identify the Error Source

Lecture 66 Provide Path Forward

Lecture 67 Summary & Exercises

Lecture 68 Watch + Learn: Delivery of AI

Lecture 69 Do + learn: Delivery of AI

Section 5: Optimization of AI

Lecture 70 Module Introduction

Lecture 71 User Expectation Management

Lecture 72 User Feedback Control

Lecture 73 Guide for User Feedback

Lecture 74 Connect Feedback with Personalization

Lecture 75 Communicate What Feedback

Lecture 76 Continuous Learning Needs

Lecture 77 AI Audits

Lecture 78 Summary & Exercises

Lecture 79 Watch + Learn: Explainability & Trust

Lecture 80 Do + Learn: Optimization of AI

Section 6: Maintenance of AI

Lecture 81 Module Introduction

Lecture 82 Why Model Maintenance?

Lecture 83 How to plan to maintain AI Models? Basic info

Lecture 84 AI Model Risks

Lecture 85 Model Monitoring

Lecture 86 Define Metrics: Business Metrics

Lecture 87 Setting Thresholds

Lecture 88 Creating Action Plan

Lecture 89 Plan for Continuous Training/Re-training

Lecture 90 Continuous Training Types

Lecture 91 Model Governance

Lecture 92 Summary & Exercises

Lecture 93 Watch + Learn: Konnect AI Governance

Section 7: Program Assessment

Lecture 94 Module Introduction

Lecture 95 Situation 1: First-Time Interaction with AI

Lecture 96 Situation 2: Error Control from AI System

Lecture 97 Situation 3: Balancing User Control and Au

Lecture 98 Situation 4: Feedback Control of AI System

Lecture 99 Situation 5: Error and User Feedback Mana

Start-up Founders,CXOs who are building AI products & solutions,Product Managers,AI Engineers who are looking to learn about overall AI building & adoption