![]() Easy deployment of high-precision models in any location.Create reproducible workflow and models.The customer has now gone through an enablement process that lets them step up their machine learning (ML) lifecycle and move to a higher level of automation with added capabilities for rapid innovation through robust machine learning lifecycle management. This included design, development, testing, deployment to production, training and post launch support. ![]() The goal of this solution was to streamline to MLOps in order to scale the solution and save costs. The data science team also integrated SageMaker Studio as an IDE to leverage purpose-built tools for ML development, like managing experiments, explainability capabilities, data visualization, and more. All stages were implemented on AWS SageMaker Pipeline, and the pipeline was wrapped in AWS native tools to control triggers and manual approvals. The solution delivered a training pipeline that controls data preparation, model training, and model deployment. ![]() CloudZone had developed a full machine learning pipeline which was designed and built on top of the AWS SageMaker platform.
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