O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 362

Marketing Experimentation Metrics Testing 362

O'Reilly on Data

MARCH 31, 2020

This means that the AI products you build align with your existing business plans and strategies (or that your products are driving change in those plans and strategies), that they are delivering value to the business, and that they are delivered on time. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

Cloudera

FEBRUARY 25, 2021

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. In our current landscape, businesses that have adopted a successful ML strategy are outperforming their competitors by over 9%. Deep Learning for Image Analysis.

Deep Learning 83

Deep Learning Machine Learning Forecasting Visualization 83

datapine

MAY 14, 2019

Best for: Those looking for a practical means of understanding how artificial intelligence serves to enhance data science and use this knowledge to improve their data analytics strategies. 2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. click for book source**.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

DataRobot Blog

MAY 26, 2022

In this post, we will dive deeper into how members from both the first and second line of defense within a financial institution can adapt their model validation strategies in the context of modern ML methods. Furthermore, due to their relative simplicity in model structure, these models were very straightforward to interpret.

Risk 52

Risk Modeling Metrics Business Objectives 52

O'Reilly on Data

DECEMBER 12, 2019

If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. That’s where remediation strategies come in. We discuss seven remediation strategies below. You’ve even discovered a few problems with your ML model. What can you do?

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Domino Data Lab

AUGUST 10, 2021

This change makes models the new currency of competitive advantage, strategy, and growth. Some popular tool libraries and frameworks are: Scikit-Learn: used for machine learning and statistical modeling techniques including classification, regression, clustering and dimensionality reduction and predictive data analysis.

Modeling 64

Modeling IT Risk Management Deep Learning 64