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
The client, a leading consulting firm, encountered the task of establishing an efficient ML model development pipeline. We created and implemented a robust data pipeline for managing large-scale data, prioritizing error identification, validation, and consolidation. We also leveraged AWS Sagemaker for seamless integration of ML frameworks, ensuring seamless scalability and architectural support through AWS API gateway.
Our solution integrated a high-speed Spark-driven data pipeline, orchestrated by Airflow as a scheduler, while structured data was stored in Snowflake or Redshift, guaranteeing optimal processing and storage. We also employed the Sagemaker framework to develop Machine Learning models, equipped with readily available, pre-built models tailored for business intelligence, supply chain risk management, and other high-impact scenarios. In addition, we streamlined the entire workflow, encompassing data transformation, ML modeling, and culminating in compelling visualizations.