Azure/MachineLearningNotebooks
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MachineLearningNotebooks
Azure Machine Learning Notebooks is a cloud-based environment for developing and executing interactive Jupyter notebooks within a managed machine learning workspace. It provides managed machine learning compute through cloud-based workstations and containerized environments pre-configured with GPU drivers and kernels for high-performance model training.
The project functions as a distributed GPU training platform and an ML experiment tracking system to monitor training metrics and version data assets. It also serves as an MLOps pipeline orchestrator for automating modular workflows and a model inference endpoint for exposing trained models as online APIs for real-time prediction and scoring.
The platform covers a broad range of capabilities including data science workspace management, the execution of containerized training jobs on GPU clusters, and the organization of versioned data assets. It also provides tools for securing development workspaces and persisting files across shared network drives.
Features
- Managed Training Environments - Provides a managed platform for running distributed training jobs on GPU clusters with pre-configured environments.
- Experiment Tracking Systems - Logs metrics, parameters, and artifacts from training runs to monitor, compare, and version model development.
- Distributed Training Platforms - Provides a platform for executing containerized training jobs across GPU clusters with managed compute resources.
- Model Training - Trains machine learning models on cloud workstations using prepared data and tracks runs for analysis.
- Cloud GPU Notebooks - Provides managed cloud workstations with pre-configured GPU drivers and kernels for interactive notebook development.
- Experiment Tracking Systems - Logs metrics, parameters, and artifacts from each training run to a central store for monitoring and comparison.
- MLOps Pipeline Automation - Assembles and automates modular machine learning workflows from reusable components that run on schedule or trigger.
- Containerized Training Jobs - Schedules containerized training jobs across GPU clusters for distributed high-performance model training.
- Data Science Workspaces - Creates and manages cloud workspaces with controlled access to data sources, compute resources, and shared file storage.
- Model Endpoint Deployments - Deploys trained models to real-time scoring endpoints for serving predictions.
- ML Pipeline Orchestrators - Assemble a machine learning workflow from reusable components that run on a schedule or trigger.
- Training Metrics - Logs metrics, parameters, and artifacts from each training run for monitoring and comparison.
- ML Asset Versioning - Organizes and versions data assets with metadata tracking for reproducibility across experiments.
- Network Drive Mounts - Mounts network drives to persist files and notebooks across sessions and compute instances.
- Workspace Provisioning - Creates secure cloud workspaces with controlled access to data sources and compute resources.
- Workspace Isolation - Creates controlled cloud workspaces with role-based access to data sources and compute resources.
- Deployment and Serving - Collection of tutorials for managing machine learning workflows on Azure.
