The following are the tools for implementing ML Pipelines on AWS
AWS Step Functions helps to build ML pipelines using AWS services like DynamoDB, Lambda, and SageMaker. Specifically, SageMaker helps to rapidly deploy machine learning models.
A very powerful open-source platform used to create, schedule, and monitor ML workflows at scale. With workflows defined as Directed Acyclic Graphs (DAG), dependency management of different tasks in the pipeline is ensured.
A Kubernetes tool specifically meant for creating and managing ML pipelines. Allows specifying DAGs, where each step of DAG is a Kubernetes pod. Python language is used for interaction with Kubeflow.
A tool that is part of Databricks, it has predefined patterns for tracking experiments and deploying models. MLFlow is a Python library that can be imported to the existing ML code and a Command Line Interface is used to train and deploy ML models to AWS SageMaker.
The following are the tools for monitoring ML models on AWS
A powerful tool that offers monitoring capabilities for all stakeholders at all value-creation points of ML lifecycle, and that works on all cloud environments. Provides a neat-and-clean, and modular observability experience to keep the ML model delivering the right value to the user.
AWS SageMaker Model Monitor sends feature specific metrics to Amazon CloudWatch, which can be used to set up dashboards and alerts and also be integrated in Sagemaker Studio.
Anodot’s automated anomaly detection helps performance monitoring of multiple ML models in production for its efficiency and prediction quality through real time dashboards and alerts.
Monitoring your ML application with Qualdo’s AI-driven anomaly detection is completely autonomous.
Please feel free to schedule a demo for data quality assessment with us or try Qualdo now using one of the team editions below.
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