Whitepapers/ebooks
-
Read now
How Can You Enhance Your Risk Models? Finding Better Signals to Feed Them
In this in-depth guide, we reveal the real reasons your machine learning risk models are falling short, what you can do to fix them — and exactly how to tackle the problem.
-
Read now
How to Improve Your Training Data for Vastly Better Machine Learning
Making your training data better is much easier than you think, and you can use several easy strategies for quick wins.
-
Read now
6 Steps to Jumpstart Machine Learning Using the Resources You Already Have
ML has gone from buzzword to business necessity, and implementing it is quickly becoming mandatory. Here are 6 easy steps to follow to get going with the resources you have.
-
Read now
Mitigating Risk With External Data, A Guide For CROs
Your organization’s risk management strategies are going to need a major overhaul. Insights from your historical data simply won’t be enough to help you assess the risks that are coming your way.
-
Read now
Using External Data to Future-Proof Your Organization and Ensure Success Today and Tomorrow
If the current economic crisis has shown the business world anything, it’s that no amount of data analysis can prepare you for the event of having the financial market flipping upside down.
-
Read now
Marketers and Data Science: Tapping Into The Data You Need to Build a Smarter Marketing Organization
In these increasingly uncertain times, marketing leaders who start thinking data science-driven will not only stay ahead of the pack but also keep their organizations afloat.
-
Read now
How Data Scientists Can Get a Seat at the Strategy Table
How can you earn yourself a seat at the table when decisions are being made? It starts with making yourself accessible and invaluable. Read how you can get started now.
-
Read now
The Essential Guide to Feature Selection
Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong.
-
Read now
Data Scientists and Augmented Data Discovery: A Match Made in Heaven
Data science automation has historically focused on hyperparameter tuning and model optimization but now it’s time to see how new tools can empower data scientists to use more and better data.
-
Read now
AI is Making BI Obsolete, and Machine Learning is Leading the Way
Why are we still hung up on BI? It’s time to embrace a paradigm that empowers us to make smarter, better predictions using real data with machine learning.
-
Read now
The Complete Guide For Data Acquisition
This complete guide breaks down data acquisition into six steps, including data provider due diligence and data provider tests to uplift your model's accuracy.
-
Read now
Feature Generation: The Next Frontier of Data Science
It's time to take feature generation - a subset of feature engineering - from an art to a science by opening up additional data sources to achieve breakthroughs in predictive models.
-
Loading More...
