When developing mobile apps it’s important to monitor errors so that you can understand your user’s experience. You need deeper insight than just a crash report because errors could cause a degraded user experience or a drop in key behavioral metrics. Your team needs to know quickly when there are production problems either with the app itself or with your backend services so you can fix the issue before more customers are impacted.

Open source has come a long way. Open source components are the building blocks of arguably every organization’s software. According to Stack Overflow’s 2018 developer survey results, nearly half of professional developers contribute to open source projects, and 40% listed contribution to open source software as part of their non-formal learning background.

If you think about it, mobile apps are among the production services most in need of real observability: nearly countless hardware platforms and operating systems in combination with your app’s code result in a dizzying matrix of possible sources for any given issue, which means you need the power of true high-cardinality search to solve your problems.

Netflix recommends Stranger Things to you because it knows you like watching sci-fi thrillers. Tinder lets you swipe right into your next date because it’s learned your interests and partner preferences. Amazon keeps showing you Fitbit because you’ve spent a considerable amount of time browsing through the fitness and wellness category.

Computer programs that talk to people aren’t new. The natural language processing program ELIZA , which played the role of a digital psychologist, first debuted way back in 1966. This early chatbot was capable of “listening” to you as you shared your life story, delivering mostly coherent, yet vague canned responses to whatever you typed in.

Automation and efficiency go hand in hand and is something that has been mentioned in IT since the 70’s. Nevertheless, 40 years on, and the majority of companies still have to internalize and embrace automated processes.

With the number of cyberattacks growing with each passing day, the need for increasingly sophisticated security systems has never been higher. Just when these ceaseless attacks had you at your wit’s end, machine learning may just restore some sanity to the cybersecurity scene.