Add Horsepower to your AI/ML Pipeline

The text discusses the challenges of training models in AI/ML projects due to IO constraints and the need for efficient storage solutions. It highlights that traditional HDD systems are not suitable for training as they are IO intensive, and data loads can severely impact pipeline latency. Furthermore, it emphasizes the importance of low latency and high throughput reads and writes for an AI/ML pipeline at scale. Aerospike technology is presented as a solution to these challenges due to its hybrid memory architecture that makes it ideal for AI/ML applications. Aerospike's efficient use of DRAM, SSD, and PMem offers high performance and cost-efficient storage for large volumes of real-time data. The text also explores options to accelerate the AI/ML pipeline using Aerospike Connect for Spark, H2O Sparkling Water, and Scikit-learn. In summary, Aerospike can help build a low latency and high throughput pipeline for AI/ML projects without compromising on budget constraints.