2.20 (02/21/2026)
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- Version 2.20: focuses on clearer, more actionable error surfacing across ingestion, training, and workflow execution—plus usability improvements across Jobs, Evaluation, and authentication pages.
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- Target-table weighting in Training Table generation: Configure row weighting directly via the training table generation plan (weight_col), producing a canonical WEIGHT column without requiring a custom weighted train table.
- Holdout set downloads (UI): Holdout value set download is available again from the Evaluation experience, including baseline-enabled flows.
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- Databricks/Serverless reliability for JSON serialization: Fixed JSON serialization for nested pydantic dataclasses (e.g., PlanMetrics) to prevent “not JSON serializable” failures in distributed execution environments.
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# Ingestion & Connectors
- Spark schema errors surfaced during ingestion/training: Schema/type mismatches now show actionable Spark errors (rather than being masked by generic “empty table” outcomes), including proper propagation from remote execution.
- Better error handling across connectors and backends: Improved summarization for Spark errors and standardized connector/backend error types to produce clearer user-facing messages.
- Training warnings now propagate on batch-size reduction: When training auto-reduces batch size due to OOM, warnings are now surfaced to users even if the job succeeds.
- Simplified training failure messages: Training-related errors are now shorter, clearer, and more actionable across key training paths.
- Retry + timeout failures now show the root cause: When an activity retries and then times out, the original failure is surfaced (not just a generic timeout).
- Correct CANCELLED status propagation: Parent jobs now show CANCELLED (not FAILED) when a decoupled child workflow is canceled.
- PublicException migration for consistent workflow error handling: Remaining training and prediction workflows now use PublicException patterns for more consistent user-facing errors and retry semantics.
- Improved fallback workflow failure messages: When a workflow fails without a PublicException, the UI now shows cleaner, more helpful fallback messaging (less internal jargon, better readability).
- PQuery error propagation improvements: Validation and backend failures propagate more cleanly so users receive more actionable PQuery failure context. [#25175]
- Jobs table + filter experience refresh: Reordered job list tables, refined filters, updated tooltips/status pills/hover styling, improved tags rendering, and made graph snapshot interactions more discoverable.
- Safari fatal error fixed: Safari no longer hits a blocking “Something went wrong” error; the UI loads (with the expected “Safari not officially supported” banner).
- Evaluation table stability on holdout download: Clicking the holdout download icon no longer changes the selected Evaluation dropdown option.
- Histogram label typo fix: Corrected “Occurrances” → “Occurrences” in the histogram chart label.
- Auth page branding update: Updated the logo and favicon on the auth page to match current branding.
v2.19 (02/02/2026)
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- Version 2.19: delivers reliability and UX improvements across Snowflake execution, model planning/validation, job diagnostics, and data quality visibility—making failures easier to understand and data issues harder to miss.
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- Snowflake app-schema execution for training table generation: Snowflake operations now prefer SNOWFLAKE_APP_SCHEMA over customer-controlled schemas, avoiding permission-related parquet read failures during training table generation.
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- More stable sample-row polling: Improved sample row fetch logic to avoid dependency-related re-fetch loops and reduce redundant polling calls, improving stability when editing tables/columns.
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# EXAMPLE TITLE# Training & Evaluation
- Full timestamp visibility in Train Table stats: Training Data Statistics now shows complete timestamp values (with consistent formatting), improving clarity around time windows and boundaries.
- Baseline metrics no longer render empty on revisit: Evaluation baseline metrics now display reliably across navigations and when using Macro avg.
- Correct multiclass F1 labeling: Removed the misleading “@0.5” threshold label from multiclass F1 metric display.
- Clearer GPU OOM failures: Training and prediction jobs now surface user-friendly GPU out-of-memory messages instead of raw CUDA/FAISS errors, with actionable guidance.
- Better ModelPlan / model planner validation: Invalid model plan YAML and hop syntax now produce clearer, more actionable errors across UI + SDK (fail fast, more specific messages).
- Consolidated validation warnings: Validation messages are cleaner, better titled, and de-duplicated/aggregated (reducing noisy repeated warnings).
- Reliable failure surfacing for Train Table + BP Table jobs: get_errors now checks multiple sources (DB + files + failure_info/log detail) so errors show even when S3 validation files aren’t written (early exceptions/cancellations).
- SDK run prediction + custom pred table failure handling in UI: Batch Prediction details now correctly shows failure state and error messaging (instead of getting stuck loading) when a custom prediction table is used.
- Job warnings & errors UX refresh: Warnings are surfaced consistently across Training/Prediction job pages (de-duplicated and aggregated from multiple sources), with improved error visual treatment.
- Jobs page spacing alignment: Updated Jobs pages spacing to match new design standards for better readability and consistency.
- Typography design tokens adoption: Jobs-related pages now use standardized typography tokens for consistent text hierarchy and styling.
- Table sample tooltip usability: Table sample tooltips now have a max height with scrolling so long values don’t obscure the UI.
- No stale table details after deletion: Deleting a table no longer leaves the UI showing persisted details for a non-existent table.
- Clearer Tags helper text: Tags helper text now clarifies that multiple tags can be added by pressing Enter (plus a related Prediction Details column fix).
- Data quality warnings made obvious: Column Statistics and Graph Link Health now highlight high missing/invalid percentages (≥10%), add explanatory tooltips, and improve clarity around dropped rows and linkage issues.
v2.18 (01/09/2026)
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- Version 2.18: delivers reliability upgrades across SPCS job execution and storage, plus UI improvements that make query editing, timestamps, and explanation visualizations more consistent and trustworthy.
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- Holdout Set Downloads: To streamline the web interface, the option to download holdout sets has been removed from the UI. Users can continue to access and download these datasets via the SDK (see documentation)
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- SPCS job status reliability: Jobs that have not started provisioning yet are now treated as pending instead of incorrectly failing during early polling.
- SPCS retry robustness (vol3 cleanup): Added job-specific folder isolation for graphengine block storage and cleanup on retries to prevent corrupted/stale data from breaking re-runs.
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# Training & Evaluation
- Holdout location now displays correctly with refit interval enabled: Fixed holdout path handling so Evaluation pages reliably show the holdout location (instead of failing due to an incorrect path).
- New UI for Models and Prediction details pages: (clearer, more consistent timestamps and explanations visibility).
- Consistent timestamp formatting: Standardized timestamp display across the UI, with full UTC timestamps available on hover tooltips.
- PQ Editor input stability: Fixed broken typing/edit/delete behavior in the Predictive Query editor to behave like a normal text editor.
- Query Editor reliability + debug console improvements: Prevented placeholder text from being inserted via Cmd+Z and improved debug console metadata visibility (e.g., version/customer id when available).
- Correct subgraph explanation rendering: Local subgraph explanations now respect the model’s hop limit so visualizations don’t show “extra hop” nodes.