Revolutionize data processing and ML deployment with accelerated performance and seamless integration with AWS, processing up to 50GB of data daily in under 3 hours

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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.


50+ GB
data processed in 3 hrs daily
Supported model development within Sagemaker environment
Seamless deployment of models to AWS cloud