AI Ecosystems

 AI Infrastructure Alliance (AIIA) is an invaluable resource for designing enterprise systems. Among many excellent resources, those focusing on methodologies for building analytics platforms are particularly valuable. Unfortunately, in academic research, there are few models for optimizing decisions when building enterprise AI and analytics #ecosystems. 

There are several approaches to building #dataanalytics platforms: in-house, end-to-end standardization, or a modular hybrid architecture where we pick and choose the best tools. Many factors influence the decision, including the #architecture of the existing legacy systems and tech debt, the availability of talent for development and integration, specialization level, the need to address changing requirements flexibly, or the cost. There are pros and cons to each approach. A well-designed in-house solution might be less reliable and generalizable on the balance of probabilities. Still, the organization will have a greater degree of in-depth control over the code and the features. On the other side, off-the-shelf solutions might provide an end-to-end platform that replaces legacy solutions. However, likely, a #standardization approach might not meet every need. Additionally, end-to-end platforms are constantly evolving, including mergers and consolidations that might complicate longer-term planning. Finally, the reality is that supporting a large number of use cases often requires different categories of functionality for data processing, pipelining, #lineage, #governance, feature stores, and #explainability, to name only a few.

In any case, any solution will likely demand significant integration work around real-time or batch processing #pipelines, APIs, ingress and egress, typically organized around an #agile management framework.

#ai #architecture #infrastructure #planning

https://ai-infrastructure.org/ai-infrastructure-ecosystem-report-of-2022/