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Model Metadata: extended substantially our core model management and metadata collection capabilities. This includes:
Standard Model Representation: expanded “standard model representation”, which is how we define and collect all of the elements that compose a model in an agnostic way, regardless of the language, framework, data science development tooling, etc.
Metadata Collection: expanded support for on-going and continual metadata collection about a model, including collecting and tracking metadata about a model’s intended usage, source code, externalized schemas, trained artifacts, dependencies, and other core elements that compose a model.
Externalized Artifacts: added support for storing large model artifacts in S3-compliant object stores, allowing organizations to leverage cost-efficient approaches to managing the large trained model artifacts (and other model artifacts) that are required for model execution for certain classes of models.
Model Test Results: added support for generic test results, allowing for the automated or manual execution of defined statistical, technical, or business testing. Data scientists and other users have the ability to define the specific metrics that they would like to see, as each model and use case vary and thus may require a specific testing approach. All test results are persisted with the specific version of the model for full auditability.
Model Reproducibility Automation: added support for automatically reproducing training and/or validation results in a target environment from the original model assets. This capability supports the ModelOps best practices to enable systematic reproducibility of core functions of a model (training, testing, scoring) to support both quality control and auditability. All reproducible runs are persisted with that specific version of the model allowing for rapid and comprehensive response to any audit inquiry.
Model Traceability: added support--via the MLC engine--to track each step in a model’s lifecycle for each version of a model to provide a full lineage of a model from when it is registered, to when it is tested, validated, approved, deployed, improved, and eventually retired.
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Extended Support:
With the release of v2.0, there a few several v1.x items that will be deprecated:begin to be deprecated over time. However, ModelOp will offer extended support for these components for licensed Customers until Customers are upgraded to v2.0.
v1.x CLI: both the Go and Python v1.x CLI will be deprecated over time with the introduction of v2.0 software. All users should download Note that the latest v2.0 CLI should be downloaded for usage with our latest v2.0 release.
v1.x Python SDK: the v1.x Python SDK will be deprecated over time with the introduction of v2.0 and instead be . The Python SDK is being replaced by the much more extensive and robust set of API’s offered via the v2.0 architecture. For more details on the v2.0 API’s, please see the v2.0 Technical Guide materials.