Feed Hungry AI Systems More Data, Faster & Efficiently

Presented by

Kiran Matty - Director of Product Management, Aerospike

About this talk

ML models run better with more data; and the more iterations and the more training, tuning and validation you can do, the better your results. So what’s the big deal? It can often be about speeding up e.g. Spark jobs for operational efficiency. The faster a job runs, the more jobs can be run on the same cluster, maximizing returns on time and investments significantly. This in turn reduces the training and inference times significantly. Data prep is painful. The problem is having an online system with streaming data and the need to make an inference in milliseconds. The problem is wanting to pull disparate signal data from sources from different countries and data centers in real-time. AI/ML systems have insatiable appetites for data. In this session, we’ll cover how to: Ingest high velocity, high volume at the edge and with “multi-site clustering” Reduce training and inference times Accelerate Spark jobs with massive parallelization Create a high throughput and low latency streaming pipeline Use fewer resources overall

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The Aerospike Real-time Data Platform enables organizations to act instantly across billions of transactions while reducing server footprint by up to 80 percent. The Aerospike multi-cloud platform powers real-time applications with predictable sub-millisecond performance from terabytes to petabytes of data with five nines uptime with globally distributed, strongly consistent data. Applications built on the Aerospike Real-time Data Platform fight fraud, provide recommendations that dramatically increase shopping cart size, enable global digital payments, and deliver hyper-personalized user experiences to tens of millions of customers. Customers such as Airtel, Experian, Nielsen, PayPal, Snap, Yahoo and Wayfair rely on Aerospike as their data foundation for the future. Headquartered in Mountain View, California, the company also has offices in London, Bangalore and Tel Aviv.