Manage:
Spark-based Model Support: support for registering and managing Spark-based models
HDFS Asset: integration of HDFS-persisted “assets” for model artifacts as well as for input/output data sets
Snowflake integration: integration of ANSI-compliant SQL ”assets” for job execution for Snowflake and other common enterprise databases (e.g. SQL Server, DB2)
SparkSQL Asset: integration of SparkSQL ”assets” to define input/output data sets for model execution (scoring, metrics jobs, etc.)
Monitor
Spark Runtime Support: integration of Spark as an ”external runtime”, whereby ModelOp Center orchestrates and manages the execution of jobs within Spark, persisting all metadata and job execution information with each version of a model
Starter Monitoring Templates: additional out-of-the-box “starter MLC’s” for Spark Execution for drift monitoring, concept drift monitoring, and backtesting
Govern:
Model Code Scanning: support for integration with Veracode for model code scanning, including verification of passing scans within a Model Lifecycle
Credentials Management: support for integration with Vault for credentials management for core services
MLC Templates: simplified out-of-the-box MLC templates that can be used/customized
User Experience:
Jobs page: updated Jobs page to allow orchestration across Spark and other model execution platforms, as well as an intuitive wizard to launch jobs
Create snapshot: provided an intuitive wizard to create snapshots
MLC Tagging: modified MLCs to be case insensitive for model tags
MLC Details: included an additional description of each MLC, including inputs/outputs and tags used
MLC UX: provided additional visibility into MLC processing, including current state and any incidents