AI and DB can benefit from each other. Hence, the project contains documentation about the two.
本项目包含AI与DB结合领域的资料，主要包括AI4DB、DB4AI以及AI in DB三大领域。
使用AI的手段来达成数据库等基础软件的自治运维能力，如索引推荐、参数调优以及故障诊断等。
通过数据库实现AI模型的训练和管理，也即 in-database machine learning.
- "Be close to your data.": Keeping the code near the data is necessary to keep the latency low; 有助于减少数据传输延迟。
- 更安全：数据统一管理，利用数据库自身严格的安全以及审计机制，确保数据泄露可能的最小化；
- 更快速：将模型优化能力整合在数据库内，充分挖掘向量化执行、GPU、NUMA等机制的性能提升；
- 更易用：提供开箱即用的AI能力，用户可以不关注算法的实现，只需关注模型的训练和使用，聚焦于业务成功；
- 易管理：防止模型存储碎片化，提供集中式的模型、数据管理能力，防止模型存储的碎片化；
- 天然的AP特性：原始OLAP能力只能提供数据的统计和分析，通过AI功能，补足OLAP系统的在预测能力上的不足。
- Amazon Redshift: Amazon Redshift ML is designed to make it easy for SQL users to create, train, and deploy machine learning models using SQL commands.
- BlazingSQL: BlazingSQL is a GPU-accelerated SQL engine built on top of the RAPIDS ecosystem; it exists as an open-source project and a paid service.
- Google Cloud BigQuery: BigQuery ML brings much of the power of Google Cloud Machine Learning into the BigQuery data warehouse with SQL syntax, without extracting the data from the data warehouse.
- IBM Db2 Warehouse: IBM Db2 Warehouse includes a wide set of in-database SQL analytics that includes some basic machine learning functionality, plus in-database support for R and Python.
- Kinetica: Kinetica provides a full in-database lifecycle solution for machine learning accelerated by GPUs, and can calculate features from streaming data.
- Microsoft SQL Server: Current versions of SQL Server can train and infer machine learning models in multiple programming languages.
- Oracle Database: Oracle Cloud Infrastructure can host data science resources integrated with its data warehouse, object store, and functions, allowing for a full model development lifecycle (AutoML, ML, DL).
- Vertica: Vertica has a nice set of machine learning algorithms built-in, and can import TensorFlow and PMML models.
- MindsDB: MindsDB brings useful machine learning capabilities to a number of databases that lack built-in support for machine learning.
- MADlib: An extension to PostgreSQL. It contains a lot of AI algorithms, not only machine learning but also deep learning and algorithm utility. PostgreSQL-based databases (e.g., Greenplum, PGXL) can be easy to integrate to get AI abilities.
- openGauss: An open-sourced database in China, which Huawei released. In the openGauss, there are some machine learning algorithms built-in. Also, it features several training utilities, such as DB4AI-snapshots.
- SQLflow: It is a SQL syntax model training tool that does not support data storage, merely simplifying programming.
- 向量化执行
- 并行执行
- 新硬件（如GPU）加速
- 高效率的AI算法集成形态（不是简单的UDF形式）
- 模型管理
- 全流程的AI工具：如超参数优化
- 提供开箱即用的SQL语法
- 分布式训练：如参数服务器、联邦学习等技术的吸收
- 高效的数据存储：如列存
- 模型的导入与导出：支持对常用框架格式的支持，如tensorflow、Pytorch
- https://www.infoworld.com/article/3607762/8-databases-supporting-in-database-machine-learning.html
- https://madlib.apache.org/
- https://opengauss.org/zh/
通过改造现有的数据库结构，融合AI相关的算法，实现数据库性能、易用性、安全性等的提升。