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.

In this blog, I am going to explain some of the best practices for building a machine learning system in Google Cloud Platform.  We'll start by showing how to understand and formulate the problem and end with tips for training and deploying the model. We'll also discuss required GCP tools, data processing in GCP, and data validation.