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Data Science platform -  Automating ML Lifecycle

AWS CodePipeline
AWS CodeBuild
AWS CloudFront
AWS SageMaker
Machine Learning
  • High project stall rate – Data scientists face difficulties in deploying ML models, with 80% or more projects stalling before completion.
  • Lack of structure in the ML lifecycle – The absence of a formalized process hinders successful model deployment.
  • Difficulty in rolling in and out machine learning models, monitor them and retrain them in a structured manner
  • Leverage the capabilities of AWS Sagemaker to create automated pipeline for preprocessing ,training, testing, deploying and monitoring Machine Learning models
  • Use SageMaker’s AutoML ability to automatically create models by providing just the dataset and target variable. Use the generated artifacts by AutoML to further fine tune the model.
  • Accelerates Machine Learning development by reducing the training time from hours to minutes with further optimized structure.
  • Streamlines the Machine Learning lifecycles by automating and standardizing MLOps practices across the organization which are using AWS services, to build, train, deploy and manage models at large scale
  • Efficiently find the optimal solutions to Machine learning problems using AutoML

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The customer faces substantial hurdles in efficiently deploying machine learning models, evidenced by a high project stall rate and a lack of structured processes throughout the ML lifecycle, prompting the need for transformative solutions to streamline model deployment and lifecycle management.