History of natural language processing

Striveworks and Figure Eight Federal Enter into Strategic Partnership for Enhanced Annotation Capabilities within Machine Learning Operations Platform

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Wednesday, April 27, 2022
Solution, National, Intelligence, Security Commission, Software, Bias, Algorithm, JAIC, Joint Artificial Intelligence Center, National Security Commission on Artificial Intelligence, Data science, History of natural language processing, Computer vision, Technology, Partnership, Goal, AI, Command, MLOps, United States Department of Defense, Chariot, Government, Artificial intelligence, Medical device, Defense

AUSTIN, Texas and ARLINGTON, Va., April 27, 2022 /PRNewswire/ -- Striveworks and Figure Eight Federal are excited to announce their strategic alliance to jointly support the government's emerging capabilities in AI technologies.

Key Points: 
  • David Poirier , President of Figure Eight Federal, said "Our efforts to assist federal customers parallels that of Striveworks and therefore we are excited to work with Striveworks to achieve our common goals."
  • Figure Eight Federal has more than 15 years of experience assisting its federal customers with their advanced annotation needs.
  • Data annotation is the process of labeling data to enable a model to make decisions and take action.
  • Figure Eight's low code annotation platform integrates with our low code Chariot MLOps platform to accelerate AI solutions for our joint customers."

OctoML Unveils Next Iteration Of ML Deployment Platform To Scale ML Operations In The Enterprise

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Thursday, December 16, 2021
Cloud, Cortex, CTO, AMD, ResNet, Madrona Venture Group, Computer vision, MLOps, CPU, Intel, YOLO, ARM Cortex-A, Microsoft, GPU, Natural language processing, Inception, ML, Nvidia Jetson, TVM, Oss, History of natural language processing, Silicon, BERT, CEO, Research, NVIDIA, Machine learning, API, CVP, NLP, Microsoft Azure, Google Cloud Platform, TensorFlow, VP, ONNX, Azure, UI, Acceleration, Automation, AWS, Video game console

SEATTLE, Dec. 16, 2021 /PRNewswire/ -- OctoML today announced the latest release of its Machine Learning (ML) Deployment Platform to empower enterprises to scale their ML operations (MLOps).

Key Points: 
  • SEATTLE, Dec. 16, 2021 /PRNewswire/ -- OctoML today announced the latest release of its Machine Learning (ML) Deployment Platform to empower enterprises to scale their ML operations (MLOps).
  • In fact, research shows that nearly two-thirds of models take over a month to deploy into production," said Luis Ceze, CEO, OctoML.
  • A number of OctoML customers are already using the new platform to power their ML model "factories" where trained ML models enter the platform and the output is a package containing that same modelaccelerated across the users' chosen deployment targets.
  • OctoML aims to accelerate model performance while enabling seamless deployment of models across any hardware platform, cloud provider, or edge device.