Squark AI Tips Archive

Do You Even Need Predictive Marketing? It comes down to insights versus actions. Predictive Marketing is the umbrella term encompassing processes that rely on AI and machine learning to drive actions with accurate predictions. This is in contrast to traditional marketing,

Hint: Focus on key performance indicators. Automated Machine Learning (AutoML) can help you make better and more timely decisions by detecting signals in data that would be impossible to see with conventional analysis. To make the most of this power

A little respect is due—in both directions. Can Automated Machine Learning (AutoML) beat serious data scientists in producing accurate predictions? People who understand data science and programming deeply, when armed with all the tools and computer resources and time they

Some AutoML systems are more automatic than others. AutoML stands for Automated Machine Learning, meaning streamlining the end-to-end process of solving problems with machine learning. Steps that need to be automated include: Data Preparation (type and dependency) Feature Engineering Model

Make a prediction that comes true. AI has not taken its seat at the decision making table for many organizations that would reap big benefits from it. That’s easy to understand, since the terminology—Artificial Intelligence, machine learning, robotic assistants, and

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

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

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.