Domino Data Lab

APRIL 3, 2019

It includes perspectives about current issues, themes, vendors, and products for data governance. My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). Enterprise Repository Era” (1990–2010) – first generation DG solutions.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

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. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 226

Modeling Machine Learning Predictive Modeling Consulting 226

Smart Data Collective

JANUARY 13, 2021

Big data has started to change the world in a lot of ways. quintillion bytes of data every single day. As scalability with big data accelerates, consumers and organizations around the world are starting to witness its impact. Every aspect of our lives has been shaped by big data to some degree.

Big Data 86

Big Data Machine Learning Data-driven Manufacturing 86

O'Reilly on Data

SEPTEMBER 19, 2023

Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.

Machine Learning 254

Machine Learning Testing Modeling Cost-Benefit 254

BizAcuity

MAY 10, 2022

Amazon strategically went with the pricing model of ‘on-demand’, allowing developers to pay only as-per their computational needs. 2007: Amazon launches SimpleDB, a non-relational (NoSQL) database that allows businesses to cheaply process vast amounts of data with minimal effort. EC2 was a more evolved version of a Virtual Machine.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

The Unofficial Google Data Science Blog

JULY 22, 2020

In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a. These MAB algorithms are great at maximizing reward when the models are perfectly specified and probabilities are accurate.

Experimentation 95

Experimentation Statistics Testing Strategy 95

CIO Business Intelligence

DECEMBER 13, 2022

Split the data among multiple machines and create a distributed system. NoSQL (“Not only SQL”) databases were invented to cope with these new requirements of volume (capacity), velocity (throughput), and variety (format) of big data. Cassandra’s scalability was impressive, but its reliability also sets it apart among databases.

Broadcasting 59

Broadcasting IoT Data-driven Software 59

Occam's Razor

SEPTEMBER 15, 2014

In blue is how much time we spent in 2010 and in blue the time spent in 2014. was the dramatic shift between 2010 to 2014 to mobile content consumption. Media-Mix Modeling/Experimentation. Media-Mix Modeling/Experimentation. I've covered the value of media-mix modeling (nee. I love media-mix modeling.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

AUGUST 28, 2017

The team at Google adopted the strategy of having a robot learn own its own (rather than programming it with pre-configured models). Some of you’ve heard the “hype” before and are deeply skeptical (AI went through a two decade long tundra where it failed to live up to every promise, until say 2010 or so).

Deep Learning 139

Deep Learning Machine Learning Marketing Strategy 139

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

There is no longer always intentionality behind the act of data collection — data are not collected in response to a hypothesis about the world, but for the same reason George Mallory climbed Everest: because it’s there. But this test is subject to "selection bias", which occurs when we analyze a group that was not randomly selected.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Smart Data Collective

OCTOBER 16, 2022

One report shows that the number of annual data breaches increased around 60% between 2010 and 2021. There are a lot of benefits of using Security Information and Event Management (SIEM) systems to protect data from hackers. A growing number of hackers are becoming very brazen and conducting some truly frightening cyberattacks.

Data Lake 135

Data Lake Behavioral Analytics Big Data Enterprise 135

O'Reilly on Data

JANUARY 25, 2021

We don’t yet have adequate tools for versioning data (though DVC is a start). Models are neither code nor data, and we don’t have adequate tools for versioning models either (though tools like MLflow are a start). We don’t think so, but we’re prepared to be wrong. Neither is challenging the dominant players.

Machine Learning 363

Machine Learning Software Testing Technology 363