The field of ML is growing up. In the past, questions like „which AI algorithm should I choose“ and „what is the best way to train it“ were the main focus of AI projects. Today, these topics are well explored. Startups and big cloud providers offer services to ease the first steps and usage of AI. In addition, we can use publicly available, pre-trained models and use transfer learning to get a sound predictive performance with less effort than building our own model from scratch.
Now, new questions get into focus: how can we bring our model actually to production in a reliable and reproducible way? How can we integrate data acquisition, training, and monitoring of our model into an automated system?