Predict energy prices accurately and quickly, eliminate proprietary ML platforms, achieve 2x data science productivity improvement, and save 100% on subscriptions with low investment

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The US Grid-Scale Battery Storage Provider faced the challenge of being locked in an expensive closed proprietary machine learning platform that was difficult to use, slow, and unable to scale. The platform did not support many useful algorithms, required data to be uploaded without data privacy or security protection, and did not allow exporting or reusing the trained models. The client identified AWS Cloud as the preferred platform but faced the challenge of not having the necessary team in place to get started. The client had a business-critical need for predictions within weeks, making the situation even more pressing.


TurboPipeAi includes a high-performance Spark-based data pipeline, with Airflow as a scheduler and structured data stored in Snowflake or Redshift, ensuring efficient processing and storage. Machine Learning models developed using the Sagemaker framework with ready-to-use, pre-built models for business intelligence, supply chain risk management, and other high value use cases. Streamline end-to-end process, from raw data to structured data, ML modeling, and ultimately to the final visualization.


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