Machine Learning and Artificial Intelligence in GCP
Looking to level up your data-driven decision making? Machine learning and artificial intelligence models in GCP can help you predict the future.
Looking to level up your data-driven decision making? Machine learning and artificial intelligence models in GCP can help you predict the future.
Ricardo del Razo, CIO and Digital Transformation Leader at Grupo SADA, talks about the challenges and successes of adopting new processes, technologies, and delivery methods in the aftermath of the pandemic.
In our previous posts, we talked about how data warehouses and data lakes can help us manage our data in a more secure, cost-effective, reliable, and scalable way, as well as how to use GCP to improve our data pipelines and their orchestration. But it doesn’t matter how rich or robust your data structure is if you cannot use it to leverage your business decision making.
Spring Cloud Stream is a framework that helps developers with data integration problems, especially in event oriented applications. The goal of the framework is to make it easy to apply modern microservices architecture patterns, decouple application responsibilities, and port business logic onto message brokers.
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.
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