Avoiding Failure in AI 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.

This course does not directly teach technical skills like programming, machine learning or data engineering concepts. To get the most knowledge from this course, you must already have a certain level of technical knowledge about your role in a data or AI team. It’s recommended to take this course after you are more comfortable with standard prerequisites for data analytics, data management or IT positions.

• 1.5 hours on-demand video
• 1 downloadable resource
• Access on mobile and TV
• Full lifetime access
• Closed captions
• Certificate of completion

1. As a data scientist, I found the content well-structured, with a deep dive into common pitfalls and effective strategies. The instructor’s expertise and real-world examples enriched my understanding on avoiding failures in AI projects. This course is a must for anyone navigating AI project challenges.
2. This course is an excellent and practical guide that teaches you how to avoid pitfalls and set up data science projects for success from a project management perspective. Highly recommend the course for anyone involved in data science solution development, from developers to project managers and product owners.
3. Comprehensive, easy to understand with good examples. I recommend this course before starting a big AI project to know what actions to take. Thank you!
4. Highly recommended to those seeking a comprehensive approach to project management, and collaboration with stakeholders.
5. what impressed me the most was the course's focus on non-technical aspects related to data science projects, which is often missing in technical data-oriented courses.