An important element of machine learning is retraining the machine learning models to compensate for 'model drift'.
Machine learning (ML) is an incredibly powerful tool with many applications. However, in order to ensure the results remain accurate over time, the data models being used by the ML need to be regularly retrained to compensate for 'model drift'. For example, customer behaviours will vary over time and as a result any model based on customer data will 'drift' if it is not retrained to reflect the changes in customer behaviours.
company Inawisdom have used Amazon Web Services (AWS) to build orchestration and workflows that address 'model drift', allowing models to be retrained and redeployed without impact to customers.
Visit the Inawisom website
to learn how they utilise AWS SageMaker as part of a solution to build, deploy and continually retrain ML models.