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Decisiv Enhances Data Architecture with Machine Learning


Data pervades all aspects of modern human life - the more data you control, the better decisions you can make. If you don't control the data, you can't control the market. And when you don't do that, somebody else does. Decisiv, a leading Cargo & Logistics company in North America, faced this exact challenge. Collecting tremendous but inaccurate data on business operations over a span of 10 years, Decisiv met nothing but excessive storage bills.

In this story, ClearScale presents how modern AWS services for Big Data and Machine Learning enabled Decisiv to solve these problems. Decisiv was able to start using data as a business asset with real value uncovered, accompanied by never-before-seen opportunities for growth.

We will discuss how AWS Glue, AWS Lake Formation, and Amazon Athena allowed building the foundational architecture for their data, used as ground truth in both current and forthcoming engagements. Next, we'll deep dive into the Amazon SageMaker stack and discuss ML Ops benefits that you won't find in traditional solutions. We'll then see how this problem can be solved with both Managed and Custom ML models, allowing us to achieve a healthy balance between accuracy and cost-efficiency.

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