AI-powered MLOps transforms predictive analytics for a supply chain leader

Challenge

A supply chain risk analytics company needed to enhance its AI-powered predictive models to anticipate logistics delays for trucks and containers. Their manual machine learning processes were slow, taking up to 3 weeks to train models, making it difficult to scale and deliver real-time insights.

Solution

We leveraged AWS MLOps tools to optimize and automate the entire machine learning pipeline. By migrating their models to AWS Sagemaker, we significantly improved scalability, introduced automated data preprocessing with AWS Elastic Map Reduce and AWS Glue, and implemented hyperparameter tuning for flexible experimentation.

This case showcases how AI and MLOps solutions can streamline complex logistics operations, empowering businesses to respond faster to uncertainties while optimizing costs. With cutting-edge AI technology, companies can scale faster, experiment with new models, and deliver more value to their clients. Ready to harness the power of AI in your supply chain? Let's get started!

Results

• Model training time reduced by 95%, from 3 weeks to just 6 hours.
• Autoscaling for real-time prediction requests, cutting costs and optimizing resources.
• Enhanced flexibility for experimenting with different models and feature sets.
• Real-time insights helped clients avoid disruptions and penalties in logistics.
• This AI-driven transformation empowered the company to deliver faster, more accurate predictive analytics, driving resilience and efficiency for its customers’ supply chains.