MLOps

Dataiku offers numerous capabilities for implementing a complete MLOps practice:

• The Flow offers full lineage and traceability on the design on models

• Deploying projects to production with full CI/CD capabilities

• Visual Machine Learning

• Real-time REST API scoring server with versioning

• Exporting models to Python, Java, MLflow and PMML for scoring outside of DSS

• Comparing models, models versions and behavior of models over time

• Performing advanced drift analysis

• Test scenarios provide the ability to test parts of a project flow, webapps and run Python unit tests.

This section focuses on the following MLOps-specific capabilities:

• Curating features in a Feature Store

• Evaluating and comparing models and model versions

• Analyzing drift

• Importing models from external Machine Learning systems

• Tracking code experiments

• Monitoring projects, endpoints and models health

• Checking Project Standards

Note

To get started with MLOps, you may wish to first try the MLOps Quick Start and other resources on MLOps and operationalization in the Knowledge Base.

• Feature Store
• Models evaluations
• Model Comparisons
• Drift analysis
• MLflow Models
• External Models
• Experiment Tracking
• Unified Monitoring
• Project Standards