Amazon SageMaker ML Model Monitoring

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Machine learning solutions are a key part of the business world. They prepare organizations for the next big thing. The world is moving towards Artificial Intelligence (or Deep Learning) where systems can learn on their own and make decisions about processes, much like humans. This will lead to greater autonomy in operations. Two key factors are supporting the transition to the age AI and Deep Learning. Organizations today produce huge amounts of data that contains valuable insights that can help them to plan their business. Cloud technology offers the computing power needed to process large data sets and extract accurate insights. Cloud technology is able to provide the computational power required to process large datasets and draw accurate insights. Organizations are therefore better positioned to explore new opportunities to improve their processes, product or service delivery, and are better equipped to do so. Machine Learning Models are created by using data samples for training, validation, and deployment. Organizations must evaluate the performance and validity of machine learning models after they have been tested, validated, tested, and deployed. This is because real-world data may change. The models may not produce the expected outcome or perform poorly over time. Machine Learning Engineers rely upon ML model monitoring techniques for better performance, data relevance and service availability. AWS, a leading cloud computing solution provider, offers the Amazon SageMaker, a fully managed Machine Learning Service. Data Scientists and Developers use this service to create machine learning solutions in cloud. This article will explain the importance of Machine Learning Model Monitor and how Amazon SageMaker monitors ML models. Learn more about the AWS training courses, which prepare professionals for AWS Certified Machine Learning Specialty certification.
What is Machine Learning Model Monitor? Data engineers, data scientists and machine learning engineers work together to develop and maintain smooth operations for machine learning solutions. Data engineers are responsible to clean and prepare the data for the Machine Learning Models. Data scientists and Machine Learning Engineers develop, train, and then test Machine Learning Models. MLOps professionals manage the deployment and management in production of the model once it is ready for production. Monitoring is necessary to evaluate the health of ML solutions, data quality, integrity, model performance, bias, and other factors. To learn more, visit our blog on AWS machine learning models.
Importantness of Machine Learning Model MonitoringThe performance and quality of a Machine Learning Model is affected once it has been put into production. The performance of ML models is affected by a few things.
Differences in production and testing environments: Data scientists and ML engineers select a smaller dataset to train Machine Learning Models. They then clean it up to make sure that the model can learn from it. Performance degradation can occur when the same model is in production. Machine learning engineers must be able to identify these differences and take the necessary steps to fix them.
Data drift is the result of a change in the environment where data are collected. The team develops a Machine Learning Model by using a subset to train it. The data used to train the model becomes less relevant over time. This can have an impact on its performance.
Data integrity: Changes in the data format can cause serious problems for the machine learning model’s performance. These changes could be related to renaming fields, adding new data categories or splitting up categories, among other things.
The Machine Learning Model Monitor tool allows the team to better manage ML solutions and identify potential issues for proactive measures. Amazon SageMaker Model monitor is one example of a tool that alerts the team when predictions are incorrect. The tool sends alerts and alarms to the team so they can take corrective actions such as retraining or data auditing. Read our blog to learn more about why data preparation is important and how data is precluded.