2019 Machine Learning Statistics 
O'Reilly on Data
MAY 21, 2019
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.
O'Reilly on Data
DECEMBER 12, 2019
For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. There are several known attacks against machine learning models that can lead to altered, harmful model outcomes or to exposure of sensitive training data. [8] 2] The Security of Machine Learning. [3]
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KDnuggets
AUGUST 14, 2019
At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.
O'Reilly on Data
FEBRUARY 18, 2020
This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%.
O'Reilly on Data
MARCH 31, 2020
If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.
Rocket-Powered Data Science
MARCH 19, 2021
One CIO said it this way , “If CIOs invested in machine learning three years ago, they would have wasted their money. This article quotes an older market projection (from 2019) , which estimated “the global industrial IoT market could reach $14.2 But if they wait another three years, they will never catch up.”
O'Reilly on Data
MARCH 20, 2019
Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks. General concerns.
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