TSMLP: Improve cross linking

- Use Intersphinx links where possible
- Use cratedb.com instead of crate.io

Author: amotl

docs/domain/timeseries/ml-primer/10-about-intro.md

@@ -455,20 +455,20 @@ support an entire MLOps lifecycle. Stay tuned for more.
 [Apache Lucene]: https://lucene.apache.org/
 [Apache Kafka]: https://kafka.apache.org/
 [Apache Spark]: https://spark.apache.org/
-[Apache Superset]: https://crate.io/docs/crate/clients-tools/en/latest/integrate/visualize.html#apache-superset-preset
+[Apache Superset]: inv:crate-clients-tools#apache-superset
 [Cloud Console]: https://console.cratedb.cloud/
 [CrateDB Admin UI]: https://cratedb.com/docs/crate/admin-ui/
-[CrateDB Cloud]: https://crate.io/products/cratedb-cloud
+[CrateDB Cloud]: https://cratedb.com/products/cratedb-cloud
 [Dask]: https://www.dask.org/
-[Data Import]: https://community.crate.io/t/importing-data-to-cratedb-cloud-clusters/1467#import-from-url-1
+[Data Import]: https://community.cratedb.com/t/importing-data-to-cratedb-cloud-clusters/1467#import-from-url-1
 [data modeling]: https://en.wikipedia.org/wiki/Data_modeling
 [ETS model]: https://www.statsmodels.org/dev/examples/notebooks/generated/ets.html
 [Merlion]: https://github.com/salesforce/Merlion
 [Merlion documentation]: https://opensource.salesforce.com/Merlion/v1.0.0/examples/anomaly/1_AnomalyFeatures.html
-[more-integrations]: https://crate.io/partners/technology-partners
+[more-integrations]: https://cratedb.com/partners/technology-partners
 [pandas]: https://pandas.pydata.org/
 [Random Cut Forest]: https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html
 [SQLAlchemy]: https://www.sqlalchemy.org/
-[Tableau]: https://crate.io/docs/crate/clients-tools/en/latest/integrate/analyze.html#business-intelligence-with-tableau
+[Tableau]: inv:crate-clients-tools#tableau
 [Time Series Analysis in Python – A Comprehensive Guide with Examples]: https://www.machinelearningplus.com/time-series/time-series-analysis-python/
 [time series modeling]: https://en.wikipedia.org/wiki/Time_series#Models

docs/domain/timeseries/ml-primer/20-mlops-cratedb-mlflow.md

@@ -467,7 +467,7 @@ mlflow.log_artifact("model")
 #### MLflow UI
 
 Visit the MLflow UI to interact with your flow run, and your artifact. You can do this by running
-`mlflow ui` in your terminal and then navigate to http://localhost:5000 in your browser.
+`mlflow ui` in your terminal and then navigate to `http://localhost:5000` in your browser.
 
 ![MLflow ui](/_assets/img/ml-timeseries-primer/mlflow-experiment.png){width=480px}
 
@@ -627,9 +627,9 @@ make it a valuable asset in the realm of time series modeling and anomaly detect
 
 [CrateDB MLflow examples]: https://github.com/crate-workbench/mlflow-cratedb/tree/main/examples
 [CrateDB MLflow handbook]: https://github.com/crate-workbench/mlflow-cratedb/blob/main/docs/handbook.md
-[database VIEWs]: https://crate.io/docs/crate/reference/en/latest/general/ddl/views.html
+[database VIEWs]: inv:crate-reference#ddl-views
 [DVC (Data Version Control)]: https://dvc.org/
-[dynamic object columns]: https://crate.io/blog/handling-dynamic-objects-in-cratedb
+[dynamic object columns]: https://cratedb.com/blog/handling-dynamic-objects-in-cratedb
 [ETS model]: https://www.statsmodels.org/dev/examples/notebooks/generated/ets.html
 [Kolmogorov-Smirnov test]: https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test
 [Kullback–Leibler Divergence]: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
@@ -640,11 +640,11 @@ make it a valuable asset in the realm of time series modeling and anomaly detect
 [MLflow documentation]: https://mlflow.org/docs/latest/index.html
 [MLOps]: https://en.wikipedia.org/wiki/MLOps
 [ml-timeseries-blog-part-1]: https://cratedb.com/blog/introduction-to-time-series-modeling-with-cratedb-machine-learning-time-series-data
-[OBJECT data type]: https://crate.io/docs/crate/reference/en/latest/general/ddl/data-types.html#object
-[Object column policy]: https://crate.io/docs/crate/reference/en/latest/general/ddl/data-types.html#object-column-policy
+[OBJECT data type]: inv:crate-reference#type-object
+[Object column policy]: inv:crate-reference#type-object-column-policy
 [Random Cut Forest]: https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html
-[Shard allocation filtering]: https://crate.io/docs/crate/reference/en/latest/general/ddl/shard-allocation.html
+[Shard allocation filtering]: inv:crate-reference#ddl_shard_allocation
 [simulating live model deployment]: https://opensource.salesforce.com/Merlion/v1.0.0/examples/anomaly/1_AnomalyFeatures.html#Simulating-Live-Model-Deployment
-[time partitioning]: https://crate.io/docs/crate/reference/en/latest/general/ddl/partitioned-tables.html
+[time partitioning]: inv:crate-reference#partitioned-tables
 [Time Series Analysis in Python – A Comprehensive Guide with Examples]: https://www.machinelearningplus.com/time-series/time-series-analysis-python/
-[window function]: https://crate.io/docs/crate/reference/en/latest/general/builtins/window-functions.html
+[window function]: inv:crate-reference#window-functions

docs/domain/timeseries/ml-primer/30-experiment-tracking-cratedb-sql.md

@@ -160,8 +160,8 @@ model = pickle.loads(blob_content)
 ```
 
 
-[BLOB data type]: https://crate.io/docs/crate/reference/en/latest/general/blobs.html
+[BLOB data type]: inv:crate-reference#blob_support
 [ml-timeseries-blog-part-1]: https://cratedb.com/blog/introduction-to-time-series-modeling-with-cratedb-machine-learning-time-series-data
 [ml-timeseries-blog-part-2]: https://cratedb.com/blog/introduction-to-time-series-modeling-with-cratedb-part-2?hs_preview=uXVBkYrk-136061503799
 [pickle format]: https://realpython.com/python-pickle-module/
-[SQL `SELECT` statements]: https://crate.io/docs/crate/reference/en/latest/sql/statements/select.html
+[SQL `SELECT` statements]: inv:crate-reference#sql-select