Machine Learning and AI

New Machine Learning (ML) and Artificial Intelligence (AI) workloads are pushing the limits of scale and processing power of distributed systems and enterprise data centers. There is considerable effort focused on increasing compute density with hardware accelerators such as GPUs, FPGAs, and even customer ASICs.

Read the Bigstream whitepaper to understand how Hyper-acceleration can shorten development and deployment cycles for machine learning and AI projects

While these accelerators show great promise in terms of processing power and computation throughput, they add a level of development complexity. These accelerators also require new associated development and design skills that can considerably add to the length of development cycles and time-to-market for new ML and AI projects.

  • Feature Extraction
  • Model Training
  • Model Deployment

For data engineers and data scientists, there are numerous opportunities for Bigstream Hyper-acceleration to speed the time-to-insight for machine learning and AI projects:

Bigstream benchmark testing shows 4X or better acceleration of ingest, compress/decompression, data and document parsing, and Spark SQL.

Read the Bigstream Benchmark Report to see how Bigstream Hyper-acceleration will transform your Spark machine learning projects.