Operationalizing AutoML with ML Ops
Operationalizing AutoML: ML Ops

Predictions are interesting on their own. They are valuable when put into production. Operationalizing AutoML – often called “ML Ops” – means putting AutoML predictions into regular workflows to change business outcomes. Here are a few ways do it. Graphical

AI Has Changed Dramatically
AI Changed Dramatically in Only 9 Months

Productive uses for AI are closer at hand than ever due to the rise of AutoML. Beginning in 2019, advancements in AI have replaced obstacles with tools to benefit from its power. Foremost among them is Automated Machine Learning (AutoML), which does

Accessible AI
Why AI for Marketing and Sales?

Follow the money to see why marketing and sales are the most common applications for AI. Instant PaybackSmall improvements in marketing and sales can produce large returns quickly. Think of the impact of gaining a few percentage points on lead conversions,

Machine Learning vs. Statistics

Statistics and machine learning differ in method and purpose. Which is superior depends upon your goals. Statistics is a subset of mathematics that interprets relationships among variables in data sets. Statisticians make inferences and estimate values based solely on data collected

How Important Is Explainability?

How Important Is Explainability? Understanding the inner workings of ML algorithms may distract from realizing benefits from good predictions. Explainability Explained With the rise of artificial intelligence has come skepticism. Mysteries of how AI works make questioning the “black box”

How are Supervised and Unsupervised Learning Different?

Showing the way vs. stumbling in the dark – there are applications for both. Supervised Supervised Learning shows AutoML algorithms sets of known outcomes from which to learn. Think of classroom drills, or giving a bloodhound the scent. Supervised learning

What Is A Confusion Matrix?

What Is A Confusion Matrix? The most aptly named AI term is actually simple. A Confusion Matrix is a table that shows how often an AI classifier gets confused predicting true and false conditions. Here is a simple example of

Creating Marketing Value With AI
Creating Marketing Value With Codeless AI

Earlier this year, Brett House of Nielsen and Judah Phillips from Squark presented at Analytics Nexus 2019. Here is a transcript of Brett’s remarks. Watch the video “Thank you, Judah. To quickly introduce myself, as Judah had mentioned, I run

What Is Overfitting and How Do I Avoid It?

What Is Overfitting? Telltale Super-Accuracy on Training Data When machine learning models show exceptional accuracy on training data sets, but perform poorly on new, unseen data, they are guilty of overfitting. Overfitting happens when models “learn” from noise in data

How Should Dates and Times be Modeled?

Factoring and Time Series How date and time features are important in models. Many data sets contain date-time fields which we hope will provide predictive value in our models. But date-time fields in the form of MM-DD-YYY HH:SS are essentially unique

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