Title

AWS re:Invent 2023 - The real-time database to build your AI future (DAT206)

Summary

Lindley Hensarling discusses the pressures of applying AI/ML in the real world, emphasizing the balance between cost and the need for data.
Computing power consumption is increasing rapidly, with significant energy use comparable to the airline industry.
AI can solve many problems, including energy consumption, but requires conscious spending and massive data handling.
Real-time data application is crucial for effective AI/ML deployment.
Aerospike is highlighted as a feature store for real-time decision-making and operational efficiency.
Amit Shah from AWS discusses the partnership with Aerospike, focusing on performance, cost optimization, and global scalability.
AWS's Nitro system and Graviton processors are mentioned as key technologies for Aerospike's performance.
Shai Peretz from Riskified shares how Aerospike supports their fraud detection platform, emphasizing scalability, reliability, cost efficiency, and performance.
Ujwal Hadwavan from Freewheel discusses how Aerospike powers their ad campaigns, highlighting the importance of low latency and high throughput.
Aerospike 7 is introduced, with improvements in in-memory workloads, compression, and hybrid memory architecture.
The future of Aerospike includes graph capabilities and vector solutions, with a focus on diverse data models and high throughput.

The increasing energy consumption of computing for AI/ML is a significant concern, and the industry must balance efficiency with the power of AI to solve global problems.
Real-time data processing is essential for businesses to make immediate and contextually accurate decisions.
Aerospike's use as a feature store for real-time decision-making in various industries, including ad tech and fraud detection, demonstrates its versatility and efficiency.
AWS's collaboration with Aerospike, leveraging AWS Nitro system and Graviton processors, showcases the importance of cloud providers in enhancing the performance and scalability of real-time databases.
The introduction of Aerospike 7 with new features for in-memory workloads and the upcoming vector solutions indicate a continuous evolution of the platform to meet the growing demands of AI/ML applications.
The use cases presented by Riskified and Freewheel illustrate the critical need for high-performance, scalable, and reliable databases in handling massive amounts of data and transactions in real-time.
The focus on efficiency, cost optimization, and global scalability by AWS and Aerospike reflects the broader industry trend towards sustainable and economically viable AI/ML solutions.