O'Reilly on Data

JULY 15, 2019

A look at how guidelines from regulated industries can help shape your ML strategy. After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

O'Reilly on Data

SEPTEMBER 19, 2023

” Each step has been a twist on “what if we could write code to interact with a tamper-resistant ledger in real-time?” “Here’s our risk model. Isn’t it nice to uncover that in a simulated environment, where we can map out our risk mitigation strategies with calm, level heads?

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

John Wheeler

MARCH 30, 2020

Integrated Risk Management (IRM) technology is uniquely suited to address the myriad of risks arising from the current crisis and future COVID-19 recovery. These efforts demonstrate the rising need for an integrated approach to risk management and highlight the following four IRM market trends.

Marketing 59

Marketing Risk Management Risk Digital Transformation 59

Domino Data Lab

JANUARY 6, 2019

I’ve been teaching data science since 2008 privately for employers – exec staff, investors, IT teams, and the data teams I’ve led – and since 2013, for industry professionals in general. What are the projected risks for companies that fall behind for internal training in data science? This is not a new gig, by any stretch.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

Jet Global

MAY 19, 2020

Organizations across various industries must respond quickly in order to maintain continuity and de-risk their operations as their functions are drastically disrupted during a crisis. The insights can help you monitor your ability over the long term and help you avoid potential financial risks. Visibility into Liquidity is Vital.

Uncertainty 98

Uncertainty Risk Management Reporting 98

Alation

AUGUST 5, 2021

When collected data fails to meet the company expectations of accuracy, validity, completeness, and consistency, it can have massive negative impacts on customer service, employee productivity, and key strategies. Why Is it Important to Have Data Quality? Quality data is key to making accurate, informed decisions. Sales might say “Sally.”

Data Quality 52

Data Quality IT Data Governance Sales 52

Domino Data Lab

AUGUST 8, 2019

2008 – Financial crisis : scientists flee Wall St. The probabilistic nature changes the risks and process required. We face problems—crises—regarding risks involved with data and machine learning in production. Some people are in fact trained to work with these kinds of risks. To wit: data science is a team sport.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

O'Reilly on Data

JANUARY 25, 2021

We’ve explored usage across all publishing partners and learning modes, from live training courses and online events to interactive functionality provided by Katacoda and Jupyter notebooks. in 2008 and continuing with Java 8 in 2014, programming languages have added higher-order functions (lambdas) and other “functional” features.

Machine Learning 363

Machine Learning Software Testing Technology 363

Jet Global

APRIL 5, 2021

Risk management. The three-statement financial model allows you to explore how your company will perform under multiple circumstances and visualize how different decisions can interact to impact the future of the company. This is achieved through thorough risk management strategies that are continually reviewed.

Modeling 98

Modeling Forecasting Metrics Finance 98