Today, we will discuss ChatGPT and the dangers of blindly trusting code generated by artificial intelligence. The same risks exist when developers intentionally copy other developers' code without analyzing the code's actual functionality. How does ChatGPT's code quality fare when it comes to security?

Did you know that only 32% of companies report realizing tangible and measurable value from their data, and only 27% ​​say data and analytics projects produce insights and recommendations that are highly actionable?

Looking to level up your data-driven decision making? Machine learning and artificial intelligence models in GCP can help you predict the future.

Are you an executive, director, or manager responsible for implementing sales analytics strategies? These four data strategies might just be the key to your business success.

Over the last few years, machine learning has garnered a great deal of attention both in academic circles and in the marketplace. We now have a huge number of applicable use cases, thanks to the fact that we're living in an era where data is the new coin. 

In the last 2 or 3 years, you may have heard that "data is the new currency." Or perhaps you've read news about the increased usage of artificial intelligence to create self-driving cars, supplement customer support, and improve product recommendation systems based on the last purchased item or the customer's shopping profile.

There's a lot of interest in learning some form of machine learning/predictive analytics or AI these days, but knowing where to start can be difficult. I recommend learning TensorFlow on Colab (along with NumPy and pandas), because TensorFlow has support for developing many standard AI/ML models quickly and Colab gives us a user-friendly online environment for experimenting.