Data in the form of tabular datasets or images can flow freely across your business and partnerships while providing maximum data security. Our AI-powered synthetic data solution takes your original data and transforms it into privacy-compliant synthetic copies. It helps businesses avoid choosing between data privacy and data utility while selecting a privacy-enhancing technology. Using the platform enables business users to maintain privacy, successfully carry out product testing, and train ML algorithms. Synthetic Data Generator is a platform that uses algorithms to create artificial data by mirroring the statistical properties of the original data without revealing any information regarding real people. The API endpoint makes batches and real-time inferences. We also use Amazon SageMaker to train custom models using the transformed data, leveraging the distributed training capability, and creating an Auto Scaling API endpoint. Data cleansing, processing, discovery, and feature engineering get completed in both the SageMaker training instance and G4 EC2 instances. An Amazon SageMaker notebook to run against the P3 instance types and G4 EC2 instances. We use Amazon S3 as a data lake to hold large data volumes such as electronic medical records (EMRs), claims, etc. Faster AI ( Virtusa and Stanford AI Laboratory collaboration).Our end-to-end solution building life cycle contains the following set of activities with automation applied at every stage: We validate the success of a model against the domain-driven use cases daily by comparing it against the ML problem (framed) and the system level resources used to support the phases of ML workloads. LOBs: The platforms contain logical groupings of solutions and use cases to solve unique business problems.Customer utilization goals determined the specific platforms available. Platform: Each offering gets categorized as a platform.These are broken down further into distinct offerings.
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