The customer, a top consulting company, was faced with the challenge of developing a high-efficiency ML model development pipeline. Design and execute a comprehensive data pipeline for large-scale data handling, emphasizing error detection, validation, and aggregation. Utilize AWS Sagemaker for ML framework implementation, ensuring compatibility with AWS API gateway for optimal scalability and architectural support.
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.
