This information may be useful if you are building an AI design team, or if you are curious about the role of designing an AI.
In this article, we'll describe the barriers to creating artificial intelligence products and how to build them in the right way.
Product managers need to make trade-offs and considerations when building products that support machine learning (ML). Different product use cases require different ML models. Therefore, the core principle of learning how to manage ML models is the key product manager skill set.
Enabling machine learning (ML) products has an ongoing collection, cleansing and analysis of data loops for input into ML models. This repetitive loop is the driving force behind the ML algorithm and enables ML products to provide useful insights for users.
Many students have also begun to consider the transformation of AI product managers, what capabilities are needed to successfully transform? I hope that students will have a general understanding of AI through this article.
In the past two years, the artificial intelligence industry has experienced an explosive growth at home and abroad. The major giants have set up their own artificial intelligence laboratories and research institutes, but we have to admit that the current emphasis on artificial intelligence is mainly focused on technical talents. In terms of talents, the talents in artificial intelligence products have not received further attention.