Announcing Reference Architecture for AI

There are tools for advanced analytics, including free ones from Google and Kaggle.

There are well-known and validated deployment architectures for applications and the cloud.

Yet the number of practical applications is still tiny, and they retained niche implementations. While the benefits of AI are clear, there are still many gaps in AI architecture that need to be filled. For example, there is a gap between analytical tools and verified architectures for real-time deployments. This gap often stems from a lack of specific reference architectures and patterns, demonstrating the trade-offs between technologies, libraries, and tools.

Let's bridge the gap in knowledge and drive a connection between science and engineering to make fast, efficient, and practical AI deployments. Three things need to be in place to build an AI product:

1. AI Product itself
2. Core Capabilities required to build AI/ML product
3. Enabling capabilities

I will use The Pattern, my [“Build on Redis” Hackathon prize-winning open source](https://github.com/applied-knowledge-systems/the-pattern) project, to illustrate how the capabilities below can be implemented and invite you to contribute or donate.

We launch in two full-featured articles - NLP ML pipeline for turning unstructured JSON text into a knowledge graph and fresh off the press Benchmarks for BERT Large Question Answering inference for RedisAI and RedisGears with Grafana Dashboards by Mikhail Volkov