O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

O'Reilly on Data

MARCH 31, 2020

Pragmatically, machine learning is the part of AI that “works”: algorithms and techniques that you can implement now in real products. We won’t go into the mathematics or engineering of modern machine learning here. Machine learning adds uncertainty. Managing Machine Learning Projects” (AWS).

Management 332

Management Machine Learning Experimentation Metrics 332

O'Reilly on Data

FEBRUARY 18, 2020

Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%. But sustained interest in cloud migrations—usage was up almost 10% in 2019, on top of 30% in 2018—gets at another important emerging trend. Still cloud-y, but with a possibility of migration.

Data-driven 335

Data-driven Software Statistics Marketing 335

O'Reilly on Data

MARCH 18, 2020

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. But what kind?

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. More knots make the learned feature transformation smoother and more capable of approximating any monotonic function.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

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. Watermark attacks. Newer types of fair and private models (e.g.,

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

Domino Data Lab

MAY 8, 2019

That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105