Transform data processing and streamline ML deployment with lightning-fast performance and effortless integration with AWS, processing data volume up to 50GB per day in less than 3 hours
Energy & Chemicals
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 solution 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.