We provide end-to-end data science consulting: from developing a vision for a problem, through machine learning models development, to visualization, and finally to production and applications that allow our customers to use their data to gain business advantage. Tech lead in a project for us is a role, not a position, so in some projects it'll be you leading the team, and in others it'll be someone else.

To achieve IA implementation success plan for a partnership, and not replacement, between humans and machines. Buy-in is crucial, of course. So educate your workforce about IA and plan for hero moments. Engage the IA experts early to ensure data quality, even if you're just starting to collect data. Depiction of the helpful AI "GERTY" from Moon (2009), directed by Duncan Jones There are several keys to implementing a reliable Intelligence Augmentation (IA) solution that scales and provides maximum value. The technology for AI and IA is the same, but the difference in the end-result can be traced to involvement of subject matter expert humans in the planning of the application and in the use of it (for the previous articles in the series, see the links at the bottom of this post).