---
title: 'Digest #2019.09.23 – Machine Learning is the word…'
description: 'MLOps, Rise of the Term: Most of us by now have heard this word frequenting
  around; MLOps, or Machine Learning Operations.. This is an interesting article on
  the rise of the term and the challenges actually faced by teams working cross-functionally
  and dealing with Machine Learning. It talks about the limitation of managed solutions
  for  […]'
url: https://canonical.com/blog/digest-2019-09-23-machine-learning-is-the-word?format=md
---

1. [Blog](https://canonical.com/blog)
2. Article

---

[anaqvi](https://canonical.com/blog/author/anaqvi "More about anaqvi")

23 September 2019

# Digest #2019.09.23 – Machine Learning is the word…

[Kubeflow News](https://canonical.com/blog/tag/kubeflow-news)

---

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[MLOps, Rise of the Term](https://towardsdatascience.com/the-rise-of-the-term-mlops-3b14d5bd1bdb): Most of us by now have heard this word frequenting around; MLOps, or Machine Learning Operations.. This is an interesting article on the rise of the term and the challenges actually faced by teams working cross-functionally and dealing with Machine Learning. It talks about the limitation of managed solutions for Machine Learning and hits close to home for Kubeflow. While working on Kubeflow and being in the community is exciting, we’ve only scratched the surface still have a long way to go. There’s also this funny info-graphic below about data scientists emailing Jupyter Notebooks to Developers for production – *For real!*

[Artificial Intelligence to Improve Enterprise Storage?](https://arstechnica.com/information-technology/2019/09/can-artificial-intelligence-make-enterprise-hybrid-storage-better/): This is an interesting article that explores the use of AI to improve storage operations in the enterprise. With data prevailing everywhere, petabytes and zettabytes of 0s and 1s, the lives of storage systems admins are becoming increasingly difficult. “When I’m trying to understand the IO pattern of an application or a workflow, one technique that I use is to capture the [strace](https://en.wikipedia.org/wiki/Strace) of the application, focusing on IO functions. For one recent application I examined, the strace output had more than 9,000,000 lines, of which a bit more than 8,000,000 lines were involved in IO. Trying to extract an IO pattern from more than 8,000,000 lines is a bit difficult.” – Jeff Layton, Nvidia Solutions Architect. Now, these tasks seem impossible for our brains but this where the mighty AI comes in. Machines champion taking large volumes of data and synthesizing it to useful chunks. Leveraging AI and ML can truly change the way we deal with, understand and use Enterprise storage.

[Apple is Building ML to Rule Them All](https://www.computerworld.com/article/3439061/apple-is-building-a-machine-learning-system-to-rule-them-ali.html): With every tech giant and pretty much every other business getting in on Machine Learning, it’s established that ML is the next big thing in the industry. Apple is coming in heavy on Machine Learning with something called Overton. “Apple claims to have a first-of-its kind solution with [Overton](https://arxiv.org/pdf/1909.05372.pdf) – it aims to enable much of the personalization of ML models to be administered by the machine, not the human.” In general terms they are creating a Machine Learning management system; the system itself learns from the environment, responds to external factors and heals and corrects the models automatically when needed. Privacy concerns aside, our Siri responses, animojis and pet portraits are going to get snazzy – among other things…

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