Energy & Chemicals

Achieve precise and rapid energy price predictions, eliminate proprietary ML platforms, realize a 2x boost in data science productivity, and save 100% on subscriptions with minimal investment

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The US Grid-Scale Battery Storage Provider encountered the issue of being confined to an expensive closed proprietary ML platform that was cumbersome, sluggish, and lacked scalability. The platform had limited algorithm support, compromised data privacy and security during data uploads, and lacked the ability to export or reuse trained models. Recognizing AWS Cloud as the preferred platform, the client faced the obstacle of lacking the required team to initiate the transition. With a critical business need for timely predictions, the situation became even more urgent.


Our product featured a robust data pipeline powered by Spark, utilizing Airflow for scheduling, and storing structured data in Snowflake or Redshift for optimized processing and storage. We leveraged the Sagemaker framework for developing Machine Learning models, equipped with ready-to-use, pre-built models tailored for business intelligence, supply chain risk management, and other valuable use cases. We streamlined the entire workflow, from raw data to structured data, ML modeling, and final visualization.


productivity improvement for data scientists
saving on platform subscription
Accurate prediction of energy prices, saving time and resources