Challenges
- 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
Solutions
- 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.
Benefits
- 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
US based Leader in consumer robots and IoT devices building
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.
