Documentation: Update backlog

docs/backlog.md

@@ -1,17 +1,36 @@
 # Backlog
 
-## Iteration +1
-- CLI shortcut for `ddl/drop.sql`
-- Add GHA recipe to build and ship OCI image
-- Release 0.1.0
-
 ## Iteration +2
-- Demonstrate use of `--backend=databricks`
-- Run an MLflow project from the given URI, using `mlflow run`
-- Explore `mlflow experiments search`
+- Bug: Fix SQL DDL files, see https://github.com/crate-workbench/mlflow-cratedb/issues/17
+- Other than the "MLflow Tracking" subsystem, is it sensible to unlock the "MLflow Model
+  Registry" subsystem as well, when possible at all?
+- https://mlflow.org/docs/latest/model-registry.html
+- Code: Refactor / break out the generic SQLALchemy polyfill patches into `crate-python` elegantly
+- Docs: Demonstrate the "Container Use" with CrateDB Cloud instead of Docker container running locally
+- Docs: Demonstrate use of `--backend=databricks`
+- Docs: Run an MLflow project from the given URI, using `mlflow run`
+- Docs: Demonstrate the "automatic logging" feature
+  https://mlflow.org/docs/latest/tracking.html#automatic-logging
+- UX: Provide a Docker Compose file, which bundles the whole trinity
+- Project: Move repository to the `crate` organization
+- Project: Release 0.2
+
+## Iteration +3
+- Examples: Add more examples, eventually using different ML libraries
+- Docs: Explore `mlflow experiments search`
+- UX: Set up Conda feedstock repository, corresponding to the upstream one
+  https://github.com/conda-forge/mlflow-feedstock
+
+## Iteration +4
+- UX: CLI shortcut for `ddl/drop.sql`
+- CI: Do not build OCI images on _each_ PR in the long run, it costs too many
+  resources. Instead, document how to run OCI builds on demand on specific
+  branches, when it is needed to ship images for testing purposes.
+
 
 ## Done
 - Use or provide wheel packages for `hashids` and `vasuki`
 - Add `versioningit`
 - Add example `tracking_merlion.py`
 - Integrate tutorial from `cratedb-examples`
+- Add GHA recipe to build and ship OCI image