Digest #2019.09.02 – The why of Kubeflow

  • AI Tales: Building Machine learning pipeline using Kubeflow and Minio – Understand the Kubeflow value proposition in an entertaining format. The story starts with Joe, the neighbourhood Machine learning enthusiast. Joe reads a few things, becomes an expert, and then the real fun begins. He quickly runs into problems with portability, DevOps, scaling, performance, and cost. Enter Kubeman (or Kubeperson?), who personifies Kubeflow, and saves the day!
  • Basics of Data Science Product Management: The ML Workflow – Another look at the complicated space that Kubeflow helps solve. “Something I quickly learned was that managing ML products is difficult because of the complexities and uncertainties involved with the different steps in the machine learning workflow” – (1) Review of related literature; (2) Data gathering & processing; (3) Model training, experimentation, & evaluation; (4) Deployment
  • Hardware Science: Researchers demonstrate all-optical neural network for deep learning – In a key step toward making large-scale optical neural networks practical, researchers have demonstrated a first-of-its-kind multilayer all-optical artificial neural network. The next generation of artificial intelligence hardware will be much faster and exhibit lower power consumption compared to today’s computer-based artificial intelligence.
  • Hardware Science: Quantum computing should supercharge this machine-learning technique – Researchers from IBM and MIT show how an IBM quantum computer can accelerate a specific type of machine-learning task called feature matching. Feature matching is a technique that converts data into a mathematical representation that lends itself to machine-learning analysis. Using a quantum computer, it should be possible to perform this on a scale that was hitherto impossible.

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