diff --git a/docs/integrations.md b/docs/integrations.md
deleted file mode 100644
index 7ec751597..000000000
--- a/docs/integrations.md
+++ /dev/null
@@ -1,192 +0,0 @@
-(integrations)=
-
-# Integrations
-
-Flyte is designed to be highly extensible and can be customized in multiple ways.
-
-```{note}
-Want to contribute an example? Check out the {doc}`Example Contribution Guide <contribute>`.
-```
-
-## Flytekit Plugins
-
-Flytekit plugins are simple plugins that can be implemented purely in python, unit tested locally and allow extending
-Flytekit functionality. These plugins can be anything and for comparison can be thought of like
-[Airflow Operators](https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/index.html).
-
-```{list-table}
-:header-rows: 0
-:widths: 20 30
-
-* - {doc}`SQL <auto_examples/sql_plugin/index>`
-  - Execute SQL queries as tasks.
-* - {doc}`Great Expectations <auto_examples/greatexpectations_plugin/index>`
-  - Validate data with `great_expectations`.
-* - {doc}`Papermill <auto_examples/papermill_plugin/index>`
-  - Execute Jupyter Notebooks with `papermill`.
-* - {doc}`Pandera <auto_examples/pandera_plugin/index>`
-  - Validate pandas dataframes with `pandera`.
-* - {doc}`Modin <auto_examples/modin_plugin/index>`
-  - Scale pandas workflows with `modin`.
-* - {doc}`Dolt <auto_examples/dolt_plugin/index>`
-  - Version your SQL database with `dolt`.
-* - {doc}`DBT <auto_examples/dbt_plugin/index>`
-  - Run and test your `dbt` pipelines in Flyte.
-* - {doc}`WhyLogs <auto_examples/whylogs_plugin/index>`
-  - `whylogs`: the open standard for data logging.
-* - {doc}`MLFlow <auto_examples/mlflow_plugin/index>`
-  - `mlflow`: the open standard for model tracking.
-* - {doc}`ONNX <auto_examples/onnx_plugin/index>`
-  - Convert ML models to ONNX models seamlessly.
-* - {doc}`DuckDB <auto_examples/duckdb_plugin/index>`
-  - Run analytical queries using DuckDB.
-* - {doc}`Weights and Biases <auto_examples/wandb_plugin/index>`
-  - `wandb`: Machine learning platform to build better models faster.
-* - {doc}`NIM <auto_examples/nim_plugin/index>`
-  - Serve optimized model containers with NIM.
-* - {doc}`Comet <auto_examples/comet_ml_plugin/index>`
-  - `comet-ml`: Comet’s machine learning platform.
-```
-
-:::{dropdown} {fa}`info-circle` Using flytekit plugins
-:animate: fade-in-slide-down
-
-Data is automatically marshalled and unmarshalled in and out of the plugin. Users should mostly implement the
-{py:class}`~flytekit.core.base_task.PythonTask` API defined in Flytekit.
-
-Flytekit Plugins are lazily loaded and can be released independently like libraries. We follow a convention to name the
-plugin like `flytekitplugins-*`, where `*` indicates the package to be integrated into Flytekit. For example
-`flytekitplugins-papermill` enables users to author Flytekit tasks using [Papermill](https://papermill.readthedocs.io/en/latest/).
-
-You can find the plugins maintained by the core Flyte team [here](https://github.com/flyteorg/flytekit/tree/master/plugins).
-:::
-
-## Native Backend Plugins
-
-Native Backend Plugins are the plugins that can be executed without any external service dependencies because the compute is
-orchestrated by Flyte itself, within its provisioned Kubernetes clusters.
-
-```{list-table}
-:header-rows: 0
-:widths: 20 30
-
-* - {doc}`K8s Pods <auto_examples/k8s_pod_plugin/index>`
-  - Execute K8s pods for arbitrary workloads.
-* - {doc}`K8s Cluster Dask Jobs <auto_examples/k8s_dask_plugin/index>`
-  - Run Dask jobs on a K8s Cluster.
-* - {doc}`K8s Cluster Spark Jobs <auto_examples/k8s_spark_plugin/index>`
-  - Run Spark jobs on a K8s Cluster.
-* - {doc}`Kubeflow PyTorch <auto_examples/kfpytorch_plugin/index>`
-  - Run distributed PyTorch training jobs using `Kubeflow`.
-* - {doc}`Kubeflow TensorFlow <auto_examples/kftensorflow_plugin/index>`
-  - Run distributed TensorFlow training jobs using `Kubeflow`.
-* - {doc}`MPI Operator <auto_examples/kfmpi_plugin/index>`
-  - Run distributed deep learning training jobs using Horovod and MPI.
-* - {doc}`Ray Task <auto_examples/ray_plugin/index>`
-  - Run Ray jobs on a K8s Cluster.
-```
-
-(flyte_agents)=
-
-## Flyte agents
-
-[Flyte agents](https://docs.flyte.org/en/latest/flyte_agents/index.html) are long-running, stateless services that receive execution requests via gRPC and initiate jobs with appropriate external or internal services. Each agent service is a Kubernetes deployment that receives gRPC requests from FlytePropeller when users trigger a particular type of task. (For example, the BigQuery agent handles BigQuery tasks.) The agent service then initiates a job with the appropriate service. If you don't see the agent you need below, see "[Developing agents](https://docs.flyte.org/en/latest/flyte_agents/developing_agents.html)" to learn how to develop a new agent.
-
-```{list-table}
-:header-rows: 0
-:widths: 20 30
-
-* - {doc}`Airflow agent <auto_examples/airflow_agent/index>`
-  - Run Airflow jobs in your workflows with the Airflow agent.
-* - {doc}`BigQuery agent <auto_examples/bigquery_agent/index>`
-  - Run BigQuery jobs in your workflows with the BigQuery agent.
-* - {doc}`ChatGPT agent <auto_examples/chatgpt_agent/index>`
-  - Run ChatGPT jobs in your workflows with the ChatGPT agent.
-* - {doc}`Databricks <auto_examples/databricks_agent/index>`
-  - Run Databricks jobs in your workflows with the Databricks agent.
-* - {doc}`Memory Machine Cloud <auto_examples/mmcloud_agent/index>`
-  - Execute tasks using the MemVerge Memory Machine Cloud agent.
-* - {doc}`OpenAI Batch <auto_examples/openai_batch_agent/index>`
-  - Submit requests for asynchronous batch processing on OpenAI.
-* - {doc}`SageMaker Inference <auto_examples/sagemaker_inference_agent/index>`
-  - Deploy models and create, as well as trigger inference endpoints on SageMaker.
-* - {doc}`Sensor <auto_examples/sensor/index>`
-  - Run sensor jobs in your workflows with the sensor agent.
-* - {doc}`Snowflake <auto_examples/snowflake_agent/index>`
-  - Run Snowflake jobs in your workflows with the Snowflake agent.
-```
-
-(external_service_backend_plugins)=
-
-## External Service Backend Plugins
-
-As the term suggests, external service backend plugins rely on external services like
-[Hive](https://docs.qubole.com/en/latest/user-guide/engines/hive/index.html) for handling the workload defined in the Flyte task that uses the respective plugin.
-
-```{list-table}
-:header-rows: 0
-:widths: 20 30
-
-* - {doc}`AWS Athena plugin <auto_examples/athena_plugin/index>`
-  - Execute queries using AWS Athena
-* - {doc}`AWS Batch plugin <auto_examples/aws_batch_plugin/index>`
-  - Running tasks and workflows on AWS batch service
-* - {doc}`Flyte Interactive <auto_examples/flyteinteractive_plugin/index>`
-  - Execute tasks using Flyte Interactive to debug.
-* - {doc}`Hive plugin <auto_examples/hive_plugin/index>`
-  - Run Hive jobs in your workflows.
-```
-
-(enable-backend-plugins)=
-
-::::{dropdown} {fa}`info-circle` Enabling Backend Plugins
-:animate: fade-in-slide-down
-
-To enable a backend plugin you have to add the `ID` of the plugin to the enabled plugins list. The `enabled-plugins` is available under the `tasks > task-plugins` section of FlytePropeller's configuration.
-The plugin configuration structure is defined [here](https://pkg.go.dev/github.com/flyteorg/flytepropeller@v0.6.1/pkg/controller/nodes/task/config#TaskPluginConfig). An example of the config follows,
-
-```yaml
-tasks:
-  task-plugins:
-    enabled-plugins:
-      - container
-      - sidecar
-      - k8s-array
-    default-for-task-types:
-      container: container
-      sidecar: sidecar
-      container_array: k8s-array
-```
-
-**Finding the `ID` of the Backend Plugin**
-
-This is a little tricky since you have to look at the source code of the plugin to figure out the `ID`. In the case of Spark, for example, the value of `ID` is used [here](https://github.com/flyteorg/flyteplugins/blob/v0.5.25/go/tasks/plugins/k8s/spark/spark.go#L424) here, defined as [spark](https://github.com/flyteorg/flyteplugins/blob/v0.5.25/go/tasks/plugins/k8s/spark/spark.go#L41).
-
-::::
-
-## SDKs for Writing Tasks and Workflows
-
-The {ref}`community <community>` would love to help you with your own ideas of building a new SDK. Currently the available SDKs are:
-
-```{list-table}
-:header-rows: 0
-:widths: 20 30
-
-* - [flytekit](https://flytekit.readthedocs.io)
-  - The Python SDK for Flyte.
-* - [flytekit-java](https://github.com/spotify/flytekit-java)
-  - The Java/Scala SDK for Flyte.
-```
-
-## Flyte Operators
-
-Flyte can be integrated with other orchestrators to help you leverage Flyte's
-constructs natively within other orchestration tools.
-
-```{list-table}
-:header-rows: 0
-:widths: 20 30
-
-* - {doc}`Airflow <auto_examples/airflow_plugin/index>`
-  - Trigger Flyte executions from Airflow.
-```
diff --git a/examples/comet_ml_plugin/comet_ml_plugin/comet_ml_example.py b/examples/comet_ml_plugin/comet_ml_plugin/comet_ml_example.py
index 2731e109b..af5c92e71 100644
--- a/examples/comet_ml_plugin/comet_ml_plugin/comet_ml_example.py
+++ b/examples/comet_ml_plugin/comet_ml_plugin/comet_ml_example.py
@@ -150,8 +150,8 @@ def main(hidden_layer_size: int = 32):
 #     dynamic-log-links:
 #       - comet-ml-execution-id:
 #           displayName: Comet
-#           templateUris: {{ .taskConfig.host }}/{{ .taskConfig.workspace }}/{{ .taskConfig.project_name }}/{{ .executionName }}{{ .nodeId }}{{ .taskRetryAttempt }}{{ .taskConfig.link_suffix }}
+#           templateUris: "{{ .taskConfig.host }}/{{ .taskConfig.workspace }}/{{ .taskConfig.project_name }}/{{ .executionName }}{{ .nodeId }}{{ .taskRetryAttempt }}{{ .taskConfig.link_suffix }}"
 #       - comet-ml-custom-id:
 #           displayName: Comet
-#           templateUris: {{ .taskConfig.host }}/{{ .taskConfig.workspace }}/{{ .taskConfig.project_name }}/{{ .taskConfig.experiment_key }}
+#           templateUris: "{{ .taskConfig.host }}/{{ .taskConfig.workspace }}/{{ .taskConfig.project_name }}/{{ .taskConfig.experiment_key }}"
 # ```