Entries by John

AI or Not?

How to Tell What’s Real Have you tried the new “AI Latteccino” at Starbucks? Absurd—and not real, of course. But don’t be surprised if food and beverage companies become the latest vendors to co-opt the Artificial Intelligence mantle. Nearly every technology company claims that AI is “at the core” of their offerings. Actually, few use […]

Squark Joins NVIDIA Inception Program

Virtual accelerator program and NVIDIA GPUs will advance AutoML for business analysts. Burlington, MA – November 5, 2019 – Squark, a software as a service (SaaS) predictive analytics provider, today announced that it has joined the NVIDIA Inception program, which is designed to nurture startups revolutionizing industries with advancements in AI and data sciences. Squark […]

Closing More at Lower Cost With Accessible AI Predictive Power

Business leaders aren’t looking for more raw information. They’re drowning in data.  Between 60% and 73% of all data within an enterprise goes unused for analytics according to Forrester.  Answers are what is in short supply. Those responsible for revenue growth have to balance the value of insights within that data, from the cost of […]

How AutoML Beats Scoring Formulas

AutoML’s ability to detect patterns and predict can out-perform algebraic formulas and Boolean logic in common tasks. Anyone who has written a formula in Excel or adjusted parameters in an online application knows how even the smallest change can produce dramatically different results. The power of mass calculation is exactly what puts algebraic and Boolean logic […]

Feature Engineering

“Features” are the properties or characteristics of something you want to predict. Machine learning predictions can often be improved by “engineering”— adjusting features that are already there or adding features that are missing. For instance, date/time fields may appear as nearly unique values that are not predictive. Breaking the single date/time feature into year, month, […]

What Are Machine Learning Hyperparameters and How Are They Used?

Parameters are functions of training data. Hyperparameters are settings used to tune model algorithm performance. In Automated Machine Learning (AutoML), data sets containing known outcomes are used to train models to make predictions. The actual values in training data sets never directly become parts of models. Instead, AutoML algorithms learn patterns in the features (columns) and […]

What is Bias in Machine Learning?

Bias occurs when ML does not separate the true signal from the noise in training data. Biases in AI systems make headlines for results such as favoring gender in hiring, recommending loans based on ethnicity, or recognizing faces differently based on race. Some of these cases were due to biases baked into the algorithms written by (human) […]

Why AutoML Beats Lead Scoring Formulas for B2B

CRM and Marketing Automation systems have offered lead scoring features for decades. The notion of using arithmetic to turn qualification criteria and behavioral data into a simple-to-consume number makes sense. Business development and sales teams always appreciate guidance on which leads to follow next to maximize productivity. In practice, few organizations do excellent lead scoring, […]

Automated Machine Learning (AutoML)

Automated Machine Leaning (AutoML) refers to systems that build machine learning models with some degree less manual coding than a data science programmer would do building models from scratch. At Squark, AutoML means absolutely no coding or scripting of any kind. This is the strongest definition of AutoML. All of the steps in making predictions […]


Hyperparameters are variables external to and not directly related to data sets of know outcomes that are used to train Machine Learning models. hyperparameter is a configuration variable that is used to optimize model performance. Automated Machine Learning (AutoML) systems such as Squark tune hyperparameters automatically. Data scientists who build models manually can write code […]