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Anodot Chief Data Scientist Ira Cohen covers the five alert settings you should adjust to get more relevant and actionable alerts for your business metrics.

Improving testing efficiency is critical for companies today. Using a fully automated CI/CD process will help get you where you need to go when it comes to automating QA.

The fast-moving digital era has brought created the need for better, more automated revenue monitoring. Companies today can count on AI to monitor revenue streams.

Widespread overuse of the terms AI/ML in marketing have managed to thoroughly confuse the meanings of these words. The trouble is that if you don’t know what AI/ML are, or what the difference is between them, then you’re that much more likely to be sold a bill of goods when you’re shopping for a product based on those technologies. 

From biased training sets to problems with input features to not understanding context (i.e., ‘dumb AI’), problems abound when it comes to correctly and accurately tracking and monitoring machine learning models.

AI and machine learning is expected to create the biggest wave of disruption in analytics, from data integration to anomaly detection and forecasting. Businesses must evaluate which areas of their data infrastructure stand to benefit and which technologies are best suited to their needs.