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 not require programming or scripting of any kind. Here are some examples.

Business Analysts
“AI is the new BI” is a theme repeated for years by journalists and product marketers. AI is now simple enough that business analysts can make reliable predictions without being data scientists or programmers. Moving from visualizing trends with BI to predicting the future—record-by-record—with AI is transforming the way businesses run. AutoML is what makes that possible.

Citizen Data Scientists
Researchers and designers who need to understand patterns know their problems and data very well, but not necessarily the AI algorithms that can extract information they need. AutoML abstracts algorithm selection and use to produce results quickly.

Data Science Professionals
AutoML does not replace the need for true data scientists, whose expertise in solving complicated AI problems cannot be replicated. Nevertheless, AI pros use AutoML to glean insights on problems to make custom work more efficient. In addition, offloading simpler BI problems to AutoML keeps them focused where they are most needed.

Conclusion: AutoML makes achieving the benefits of AI simpler for everyone.

Classification Types and Uses

Classifications are the most frequently used—and most useful—prediction types.

Classifications are predictions that separate data into groups. Binary Classification produces “yes-no” or “in-out” answers when there are only two choices. Multi-Class Classification applies when there are three or more possibilities and shows probabilities for each.

Binary Examples

  • Churn – Which customers are in danger of leaving? If you knew, you could implement targeted retention tactics such as special offers or customer service outreach.
  • Conversion – Which prospects are most likely to be ready to move to the next stem in the buying cycle? Knowing means focusing sales resources on the best leads.
  • Risk – Which populations are likely to experience negative outcomes? Understanding helps guide actions to mitigate risks.

Multi-Class Examples

  • Cross-Sell/Up-Sell – Which customers are most likely to buy which additional products or services? Targeting them with the right offers lifts sales with high efficiency.
  • Personalization – Which content will resonate with which person? Optimizing websites, social media, and email is easy when you know.
  • Ad Targeting – Which prospects are most likely to respond to your multiplicity of ads and media? Spending is more effective when you know your audiences.

The take-away: Classifications are among the most accessible and highest-return prediction types for AutoML. Predictions on each row include not only the classes, but the probabilities associated with each. Think of a burning classification question and Squark can help you begin predicting right away.