The Future of Demand Forecasting

Understanding demand with AI-driven forecasting

With an ever changing and dynamic world, traditional forecasting techniques are being replaced with AI-driven forecasting techniques. According to McKinsey, applying AI-driven forecasting can reduce errors by between 20-50% and reduce losses in sales and product unavailability up to 65%.

Whether it’s predicting the need for parts to purchase for machine maintenance, how many items will sell in a month, how much of an ingredient to stock, or sizing of certain apparel depending on the season, it amounts to hundreds of thousands of forecasts trained on a periodic basis.

Being able to do this effectively is key for organizations to transform their business to reduce expenses and waste while increasing margins.

What you need in a platform to speed and scale demand forecasting?

Train, test, deploy, serve, and monitor machine learning models efficiently and with speed with Ray and Anyscale.

  • Optimize with minimal code changes

    Organizations must be able to operationalize and train thousands of forecasts using as few scripts efficiently. One code snippet would be best.

  • Horizontal scale for cost reduction

    Big costs can arise when vertical scaling is maxed out and big machines are necessary. Organizations need an autoscaler that requires minimal code changes to optimize cost and performance.

  • Efficient training

    What would take days or months to train thousands of models now takes minutes or hours using Ray. Continuously train models at scale to power your mission critical business in near real time.

“Before Ray, we used 10 containers with celery running on AWS Batch and it used to take 2-3 days to train ~8000 models weekly for our marketplace use case. After doing a quick POC with Ray, we are now able to train 1000 models in 20 min.”

Grocery delivery service company


“We did an internal benchmark for our forecast factory use cases and we found Anyscale gives us a 10x better performance or saves us 90% runtime overall compared to SageMaker.”

Manufacturing conglomerate

Product manager

“We used Ray to solve our demand prediction use case and we obtained some astonishing results. Specifically, compared to our AWS Batch implementation, our Ray implementation is 9x faster and has reduced the cost by 87%.”


How Anastasia accelerated their demand prediction with Ray and Anyscale

Read how a demand prediction problem was solved and led to astonishing results — 9x faster with a cost reduction the cost by 87%.

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