In the last year, the world's buzzing about Machine Learning, with a lot of investments in this area creating new job opportunities, teams and an higher pressure to deliver. Machine Learning Engineering and Data Science Teams share a lot of challenges when compared to more "classical" software engineering counterparts but also add new ones, related to the different technological nature of their deliverables, the time horizons related to some of the projects and the different skills and roles involved in their execution.
In this talk we will go through the challenges, concepts and ideas to be applied in companies of different sizes to understand own level of maturity and try to introduce reliable frameworks to grow project after project.

What does it mean to be "mature" in shipping ML projects in the industry in the first place?