# [0.61.0](
v0.60.0...v0.61.0) (2026-04-07)
### Bug Fixes
* Add grpcio dependency group to transformation server Dockerfile ([
2c2150a](
2c2150a))
* Add https readiness check for rest-registry tests ([
ea85e63](
ea85e63))
* Add website build check for PRs and fix blog frontmatter YAML error ([
#6079](
#6079)) ([
30a3a43](
30a3a43))
* Added missing jackc/pgx/v5 entries ([
94ad0e7](
94ad0e7))
* Added MLflow metric charts across feature selection ([
#6080](
#6080)) ([
a403361](
a403361))
* Check duplicate names for feature view across types ([
#5999](
#5999)) ([
95b9af8](
95b9af8))
* Fix integration tests ([
#6046](
#6046)) ([
02d5548](
02d5548))
* Fix missing error handling for resource_counts endpoint ([
d9706ce](
d9706ce))
* Fix non-specific label selector on metrics service ([
a1a160d](
a1a160d))
* fix path feature_definitions.py ([
7d7df68](
7d7df68))
* Fix regstry Rest API tests intermittent failure ([
d53a339](
d53a339))
* Fixed IntegrityError on SqlRegistry ([
#6047](
#6047)) ([
325e148](
325e148))
* Fixed intermittent failures in get_historical_features ([
c335ec7](
c335ec7))
* Fixed pre-commit check ([
114b7db](
114b7db))
* Fixed the intermittent FeatureViewNotFoundException ([
661ecc7](
661ecc7))
* Fixed uv cache permission error for docker build on mac ([
ad807be](
ad807be))
* Fixes a `PydanticDeprecatedSince20` warning for trino_offline_store ([
#5991](
#5991)) ([
abfd18a](
abfd18a))
* Handle existing RBAC role gracefully in namespace registry ([
b46a62b](
b46a62b))
* Ignore ipynb files during apply ([
#6151](
#6151)) ([
4ea123d](
4ea123d))
* Integration test failures ([
#6040](
#6040)) ([
9165870](
9165870))
* Mount TLS volumes for init container ([
080a9b5](
080a9b5))
* **postgres:** Use end_date in synthetic entity_df for non-entity retrieval ([
#6110](
#6110)) ([
088a802](
088a802)), closes [
#6066](
#6066)
* Ray offline store tests are duplicated across 3 workflows ([
54f705a](
54f705a))
* Reenable tests ([
#6036](
#6036)) ([
82ee7f8](
82ee7f8))
* SSL/TLS mode by default for postgres connection ([
4844488](
4844488))
* Use commitlint pre-commit hook instead of a separate action ([
35a81e7](
35a81e7))
### Features
* Add Claude Code agent skills for Feast ([
#6081](
#6081)) ([
1e5b60f](
1e5b60f)), closes [
#5976](
#5976) [
#6007](
#6007)
* Add complex type support (Map, JSON, Struct) with schema validation ([
#5974](
#5974)) ([
1200dbf](
1200dbf))
* Add decimal to supported feature types ([
#6029](
#6029)) ([
#6226](
#6226)) ([
cff6fbf](
cff6fbf))
* Add feast apply init container to automate registry population on pod start ([
#6106](
#6106)) ([
6b31a43](
6b31a43))
* Add feature view versioning support to PostgreSQL and MySQL online stores ([
#6193](
#6193)) ([
940e0f0](
940e0f0)), closes [
#6168](
#6168) [
#6169](
#6169) [
#2728](
#2728)
* Add materialization, feature freshness, request latency, and push metrics to feature server ([
2c6be18](
2c6be18))
* Add metadata statistics to registry api ([
ef1d4fc](
ef1d4fc))
* Add non-entity retrieval support for ClickHouse offline store ([
4d08ddc](
4d08ddc)), closes [
#5835](
#5835)
* Add OnlineStore for MongoDB ([
#6025](
#6025)) ([
bf4e3fa](
bf4e3fa)), closes [
golang/go#74462](
golang/go#74462)
* Add Oracle DB as Offline store in python sdk & operator ([
#6017](
#6017)) ([
9d35368](
9d35368))
* Add RBAC aggregation labels to FeatureStore ClusterRoles ([
daf77c6](
daf77c6))
* Add ServiceMonitor auto-generation for Prometheus discovery ([
#6126](
#6126)) ([
56e6d21](
56e6d21))
* Add typed_features field to grpc write request (([
#6117](
#6117)) ([
#6118](
#6118)) ([
eeaa6db](
eeaa6db)), closes [
#6116](
#6116)
* Add UUID and TIME_UUID as feature types ([
#5885](
#5885)) ([
#5951](
#5951)) ([
5d6e311](
5d6e311))
* Add version indicators to lineage graph nodes ([
#6187](
#6187)) ([
73805d3](
73805d3))
* Add version tracking to FeatureView ([
#6101](
#6101)) ([
ed4a4f2](
ed4a4f2))
* Added Agent skills for AI Agents ([
#6007](
#6007)) ([
99008c8](
99008c8))
* Added CodeQL SAST scanning and detect-secrets pre-commit hook ([
547b516](
547b516))
* Added odfv transformations metrics ([
8b5a526](
8b5a526))
* Adding optional name to Aggregation (feast-dev[
#5994](
#5994)) ([
#6083](
#6083)) ([
56469f7](
56469f7))
* Created DocEmbedder class ([
#5973](
#5973)) ([
0719c06](
0719c06))
* Extended OIDC support to extract groups & namespaces and token injection with multiple methods ([
#6089](
#6089)) ([
7c04026](
7c04026))
* Feature Server High-Availability on Kubernetes ([
#6028](
#6028)) ([
9c07b4c](
9c07b4c)), closes [Hi#Availability](
https://github.com/Hi/issues/Availability) [Hi#Availability](
https://github.com/Hi/issues/Availability)
* **go:** Implement metrics and tracing for http and grpc servers ([
#5925](
#5925)) ([
2b4ec9a](
2b4ec9a))
* Horizontal scaling support to the Feast operator ([
#6000](
#6000)) ([
3ec13e6](
3ec13e6))
* Making feature view source optional (feast-dev[
#6074](
#6074)) ([
#6075](
#6075)) ([
76917b7](
76917b7))
* Replace ORJSONResponse with Pydantic response models for faster JSON serialization ([
65cf03c](
65cf03c))
* Support arm docker build ([
#6061](
#6061)) ([
1e1f5d9](
1e1f5d9))
* Support distinct count aggregation [[
#6116](
#6116)] ([
3639570](
3639570))
* Support HTTP in MCP ([
#6109](
#6109)) ([
e72b983](
e72b983))
* Support nested collection types (Array/Set of Array/Set) ([
#5947](
#5947)) ([
#6132](
#6132)) ([
ab61642](
ab61642))
* Support podAnnotations on Deployment pod template ([
1b3cdc1](
1b3cdc1))
* Use orjson for faster JSON serialization in feature server ([
6f5203a](
6f5203a))
* Utilize date partition column in BigQuery ([
#6076](
#6076)) ([
4ea9b32](
4ea9b32))
### Performance Improvements
* Online feature response construction in a single pass over read rows ([
113fb04](
113fb04))
* Optimize protobuf parsing in Redis online store ([
#6023](
#6023)) ([
59dfdb8](
59dfdb8))
* Optimize timestamp conversion in _convert_rows_to_protobuf ([
33a2e95](
33a2e95))
* Parallelize DynamoDB batch reads in sync online_read ([
#6024](
#6024)) ([
9699944](
9699944))
* Remove redundant entity key serialization in online_read ([
d87283f](
d87283f))
What this PR does / why we need it:
Added image to feast + mlflow blog post, showing MLflow's metric charts view with model metrics (accuracy, AUC, F1, precision, recall) grouped by num_features.