O'Reilly on Data

DECEMBER 12, 2019

Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Figure 1 illustrates an example adversarial search for an example credit default ML model.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. The accuracy of any predictive model approaches 100%.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 86

Machine Learning Modeling Metadata Recreation/Entertainment 86

IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Session Summary. Transcript. Feel free to email me.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. We will go through a typical ML pipeline, where we do data ingestion, exploratory data analysis, feature engineering, model training and evaluation. This is to prevent any information leakage into our test set. 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Alation

APRIL 14, 2022

A useful feature for exposing patterns in the data. Supports the ability to interact with the actual data and perform analysis on it. Automatic sampling to test transformation. Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. Visual Profiling. Scheduling.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

CIO Business Intelligence

MARCH 22, 2023

And even if they’re used, they may not be enough to prevent an attacker from inferring sensitive information by analyzing encrypted or masked data patterns. The new class often uses advanced techniques such as deep learning, natural language processing, and computer vision to analyze and extract insights from the data.

Machine Learning 105

Machine Learning Cost-Benefit Modeling Risk 105

DataRobot Blog

DECEMBER 15, 2022

Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning. To accelerate the process, you can also increase the number of modeling workers (number of jobs running at the same time).

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning. show_in_notebook().

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

O'Reilly on Data

MARCH 24, 2020

Why is high-quality and accessible data foundational? The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. Generating data with a pre-specified analysis plan and running that analysis is good. Re-analyzing existing data is often very bad.”

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346