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

IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. Python is the most common programming language used in machine learning.

Machine Learning 61

Machine Learning Data Science Statistics Deep Learning 61

Cloudera

FEBRUARY 16, 2022

They define each stage from data ingest, feature engineering, model building, testing, deployment and validation. Figure 04: Applied Machine Learning Prototypes (AMPs). Given the complexity of some ML models, especially those based on Deep Learning (DL) Convolutional Neural Networks (CNNs), there are limits to interpretability.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

IBM Big Data Hub

OCTOBER 16, 2023

ML also provides the ability to closely monitor a campaign by checking open and clickthrough rates, among other metrics. Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection. Then, it can tailor marketing materials to match those interests.

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

Smarten

APRIL 19, 2024

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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. from sklearn import metrics. This is to prevent any information leakage into our test set. Model training.

Statistics 69

Statistics Machine Learning Modeling Metrics 69