Machine learning (ML) is everywhere, and there is a lot of material that discusses many of the difficulties associated with creating an ML solution from a data science point of view. This work covers aspects related to obtaining data, selecting a particular algorithm, and training and testing the algorithms. There is, however, less information related to architecting the software system where this algorithm will be running once it is in production. In this talk, we want to address this topic, both from a theoretical and practical point of view. Our goal is to help software architects that need to design systems that support ML by identifying common architecture design considerations for various phases of data processing, training, and model serving.
We will cover the following topics:
1. Where does software architecture fit in systems that support ML?
2. A framework for gathering primary architectural decisions of systems that support ML. These decisions include aspects such as
* type of training of the model and model location
* time of training: offline vs. online
* time of prediction: batch vs. on demand
* location of prediction: cloud vs. device
* technological choices
* other considerations
3. Several case studies of systems developed at SoftServe that support ML using the previously discussed framework.
4. Synthesis: What needs to be considered when architecting ML systems?
* Lessons learned
* Design process considerations
See the slides.Watch the video.
