It’s time for data science to shine. But unfortunately, for many businesses data science advances are still in the future. Despite making big investments in data science teams, many are still not seeing the value they expected. Why?
Today we’ll be discussing machine learning, data lakes, and how they can help you exploit data about your business, your customers, your partners, and anything else you need to get that competitive edge. Essentially, we’re here today to say—what’s the extra information you need to get ahead?
At your company, you can create the most elegant machine learning model anyone has ever seen. It just won’t matter if you never deploy and operationalize it. That's no easy feat, which is why we're presenting you with seven machine learning best practices.
There are many different ways to build a recommendation engine and most will combine multiple techniques or approaches. In this article, I want to cover just one approach, association rules, which are fairly easy to understand and require minimal skills in mathematics. If you can work with simple percentages, there’s nothing more complex than that here.
In this machine learning use case, an Oracle customer discovered the right solution for their motherboard failures. They turned to a machine-learning pattern recognition algorithm called Multivariate State Estimation Technique (MSET).
The rewards of big data can be compelling. At the same time, you'll want to consider machine learning challenges before you start your own project. In this article, we cover challenges which include addressing the skills gap, knowing how to manage your data, and operationalizing your data.