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Avoiding Failure in AI Projects

Business

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Avoiding Failure in AI Projects, available at $19.99, has an average rating of 5, with 21 lectures, 8 quizzes, based on 6 reviews, and has 21 subscribers.

You will learn about Strategies on how to kick-off and manage AI projects Tips to obtain business/process understanding to enhance business engagement Efficient deployment of AI projects to transform the business Application of FMEA and ChatGPT to assess project risks and develop mitigation plans This course is ideal for individuals who are Anyone who wants to know about challenges of AI projects. or Team leads and project managers involved in deployment of digital products or Data Scientists and data engineers who want to to be aware of non-technical challenges to adjust their work or Students who want to learn about real-world challenges of deploying an AI product or IT professionals who support data science projects It is particularly useful for Anyone who wants to know about challenges of AI projects. or Team leads and project managers involved in deployment of digital products or Data Scientists and data engineers who want to to be aware of non-technical challenges to adjust their work or Students who want to learn about real-world challenges of deploying an AI product or IT professionals who support data science projects.

Enroll now: Avoiding Failure in AI Projects

Summary

Title: Avoiding Failure in AI Projects

Price: $19.99

Average Rating: 5

Number of Lectures: 21

Number of Quizzes: 8

Number of Published Lectures: 21

Number of Published Quizzes: 8

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: $94.99

Quality Status: approved

Status: Live

What You Will Learn

  • Strategies on how to kick-off and manage AI projects
  • Tips to obtain business/process understanding to enhance business engagement
  • Efficient deployment of AI projects to transform the business
  • Application of FMEA and ChatGPT to assess project risks and develop mitigation plans

Who Should Attend

  • Anyone who wants to know about challenges of AI projects.
  • Team leads and project managers involved in deployment of digital products
  • Data Scientists and data engineers who want to to be aware of non-technical challenges to adjust their work
  • Students who want to learn about real-world challenges of deploying an AI product
  • IT professionals who support data science projects

Target Audiences

  • Anyone who wants to know about challenges of AI projects.
  • Team leads and project managers involved in deployment of digital products
  • Data Scientists and data engineers who want to to be aware of non-technical challenges to adjust their work
  • Students who want to learn about real-world challenges of deploying an AI product
  • IT professionals who support data science projects

With the growing recognition of data’s value and the potential of machine learning and artificial intelligence, organizations are eager to leverage these technologies. However, a staggering 87% of AI projects fail to make it into production, highlighting the need for a systematic approach to avoid pitfalls and make informed career decisions. This course aims to increase awareness about the most common pitfalls of AI projects and provide a strategic guideline to reduce the risk of failure and enhance user-engagement.

The course covers essential strategies for effective AI project management, including project kick-off, obtaining business/process understanding, and efficient deployment of AI solutions. Participants will learn about the importance of clear project goals and deliverables, domain knowledge integration, and communication and collaboration with stakeholders. Practical insights on end-user engagement, quantifying project success, and applying Failure Mode and Effect Analysis (FMEA) and chatGPT for risk assessment and mitigation are also provided.

While the course does not directly teach technical skills like programming or machine learning, it is recommended for individuals with a certain level of technical knowledge in data analytics, data management, or IT positions.

By the end of the course, participants will have gained valuable knowledge and practical tools to reduce the risk of failure, enhance user engagement, and make informed decisions throughout the project lifecycle. The course equips learners with strategies to transform AI projects into successful endeavors that align with business objectives and deliver tangible benefits. With this newfound expertise, participants can take their projects and teams to the next level in the dynamic landscape of data science.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction and Motivation

Chapter 2: Importance of Clear Project Goals and Deliverables

Lecture 1: Key Failure Modes in AI Projects

Chapter 3: Strategies for Effective AI-Centered Project Planning and Management

Lecture 1: Lecture Introduction and Overview

Lecture 2: Generic Guideline for Successful AI Project Planning

Chapter 4: Importance of Domain Knowledge in AI Initiatives

Lecture 1: Lecture Introduction and Overview

Lecture 2: Techniques for Developing Process Understanding

Lecture 3: Case Study of Successful AI Projects that Leveraged Process Understanding

Chapter 5: Efficiently Deploying AI Products: Scalability, Practicality & Maintainability

Lecture 1: Overview of the Importance of Efficient Deployment

Lecture 2: Practicality Checklist for an AI Product

Lecture 3: Scalability of AI Products: Challenges and Strategies

Lecture 4: Best Practices for Maintenance of AI Product

Chapter 6: Effective Communication and Collaboration with Stakeholders

Lecture 1: Lecture Introduction and Overview

Lecture 2: Strategies for Effective Communication and Collaboration

Chapter 7: Transforming Work Processes by End-User Engagement

Lecture 1: Lecture Introduction and Overview

Lecture 2: Strategies to Engage End-Users in AI Projects

Chapter 8: Quantifying Success in AI Projects: Challenges and Strategies

Lecture 1: Case Study: AI-Based Store Operation Pilot

Chapter 9: Application of Failure Mode and Effect Analysis (FMEA) in AI Projects

Lecture 1: Lecture Introduction and Overview

Lecture 2: Assessing Severity, Occurrence, and Detectability of Failure Modes

Lecture 3: Case study of FMEA in AI Projects

Chapter 10: Integration of ChatGPT in Project Workflows

Lecture 1: How ChatGPT can Help with Project Workflows?

Lecture 2: Case Study: Detection Software to Identify COVID-19 Cases

Instructors

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  • 5 stars: 6 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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