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Fundamentals of ML observability
Metrics, feature importance and more
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Introduction and agenda >
An MLOps (Machine Learning Operations) platform is a set of tools, frameworks, and practices that streamline the process of deploying, monitoring, and maintaining machine learning models in production. It helps bridge the gap between data science and IT operations by automating various tasks involved in the machine learning lifecycle. The goal of an MLOps platform is to ensure that machine learning models are efficiently integrated, managed, and scaled within an organization while maintaining quality and performance.
MLOps platforms support ML engineers by streamlining and automating the processes involved in the machine learning lifecycle. These benefits include:
MLOps platforms typically offer the following features:
SageMaker is a fully managed MLOps platform provided by Amazon Web Services (AWS). It simplifies and accelerates the end-to-end ML lifecycle, offering capabilities such as data preprocessing, model training, hyperparameter tuning, deployment, and monitoring. SageMaker integrates with other AWS services and popular open-source frameworks.
Offered by Microsoft, Azure Machine Learning is an MLOps platform that streamlines the development, deployment, and management of ML models. It provides a wide range of tools and services for collaboration, experiment tracking, model versioning, automated ML, and deployment to the cloud or edge devices. It supports popular frameworks like TensorFlow and PyTorch, and seamlessly integrates with other Azure services.
This MLOps platform from Google Cloud combines Google’s AI and ML technologies to simplify and accelerate ML workflows. It offers a unified environment for building, training, and deploying models using popular frameworks like TensorFlow, PyTorch, and scikit-learn. Key features include data preprocessing, distributed training, hyperparameter tuning, model deployment, and monitoring, all accessible through a web-based interface.
An open-source MLOps platform created by Databricks, MLflow provides a modular approach to streamline the ML lifecycle. It offers tools for experiment tracking, project packaging, model versioning, and model deployment. MLflow supports a wide range of ML libraries and frameworks and can be deployed on-premises or in the cloud.
Developed by Google, TFX is an end-to-end platform for deploying production ML pipelines using TensorFlow. It provides a suite of components to manage data validation, preprocessing, model training, evaluation, and deployment. TFX integrates with popular data processing systems like Apache Beam and Apache Flink, and can be deployed on various platforms, including Google Cloud, AWS, and on-premises infrastructure.
Aporia fits into the MLOps platform as a crucial component in the model monitoring and management stage. In a typical MLOps pipeline, different stages include data ingestion, data preprocessing, model training, model validation, model deployment, and continuous monitoring. Aporia comes into play during the continuous monitoring phase, after a machine learning model has been deployed to a production environment.
Aporia’s advanced monitoring tools enable ML engineers and data scientists to keep a close watch on their models’ performance metrics, track potential data drift, detect anomalies, and understand the reasoning behind model predictions. This continuous monitoring ensures that the models maintain their expected performance levels and that any anomalies are quickly identified and addressed. By integrating Aporia into the MLOps platform, organizations can automate the monitoring and maintenance of their machine learning models, significantly enhancing their ability to scale AI applications and maintain their effectiveness in rapidly changing environments. Aporia empowers organizations with key features and tools to ensure high model performance:
Root Cause Investigation
To get a hands-on feel for our ML observability platform, we recommend: