Mistakes To Avoid as an AI Practitioner in Industry

She discusses the importance of knowing when AI is actually the appropriate solution, the value of domain expertise on a project, and other key factors in successful AI applications. I'm going to tell you mistakes to avoid if you want to be an AI practitioner in the industry, especially if you are coming from an academic mindset. Around 90% of total machine learning models that we build in a company or in a research lab, don't make it to production. One in ten data scientists' AI solutions end up being a part of products. Nine of the data scientists' solutions either get discarded, discontinued, or have to pivot. I will highlight twelve mistakes that are really crucial to avoid if you want to make a successful deployment to the production of an AI-based solution.