The advent of digital technologies requires industries, including shipping services, to meet the demands of global e-commerce. Accurate tracking, efficient inventory management, and seamless deliveries are now essential components that define successful shipping providers. Shipping providers turn to advanced tools to navigate the complexities of contemporary logistics. These solutions help automate workflows, minimize delays, and optimize customer service.

The value proposition of embedded analytics in 2024 has never been clearer: real-time insights that help your user base make smarter decisions, while enhancing their engagement with both data analytics as a tool, and your product or business systems. But what about the costs?

Bringing machine learning (ML) models into production is often hindered by fragmented MLOps processes that are difficult to scale with the underlying data. Many enterprises stitch together a complex mix of various MLOps tools to build an end-to-end ML pipeline. The friction of having to set up and manage separate environments for features and models creates operational complexity that can be costly to maintain and difficult to use.