O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. There are at least four major ways for data scientists to find bugs in ML models: sensitivity analysis, residual analysis, benchmark models, and ML security audits. Sensitivity analysis.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

BizAcuity

APRIL 6, 2022

Hence, a lot of time and effort should be invested into research and development, hedging and risk management. To predict movements and volatility, machine learning and deep learning algorithms are widely used by organizations to strategize and prepare accordingly.

Data Analytics 52

Data Analytics Analytics B2B Deep Learning 52

Ontotext

FEBRUARY 26, 2024

The answers to these foundational questions help you uncover opportunities and detect risks. Risk management : Understanding the correlation between events and stock price fluctuations helps manage risk. Investors make informed decisions about buying, holding, or selling stocks by analyzing these events.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Domino Data Lab

APRIL 3, 2019

The longer answer is that in the context of machine learning use cases, strong assumptions about data integrity lead to brittle solutions overall. Probably the best one-liner I’ve encountered is the analogy that: DG is to data assets as HR is to people. Data is on the move. We keep feeding the monster data.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84