docs: Change to sentence case (#4222)

Change to sentence case

Signed-off-by: Sherlock113 <[email protected]>

docs/source/bentocloud/best-practices/cost-optimization.rst

@@ -1,5 +1,5 @@
 =================
-Cost Optimization
+Cost optimization
 =================
 
 This document contains a list of best practices for optimizing costs on BentoCloud. You can use the information on this page as

docs/source/bentocloud/best-practices/index.rst

@@ -1,5 +1,5 @@
 ==============
-Best Practices
+Best practices
 ==============
 
 This section contains a list of best practices for BentoCloud usage.

docs/source/bentocloud/getting-started/index.rst

@@ -1,5 +1,5 @@
 ===========
-Get Started
+Get started
 ===========
 
 Get started with your BentoCloud journey by understanding the platform and deploying a simple application on it.

docs/source/bentocloud/how-tos/deploy-bentos.rst

@@ -1,40 +1,37 @@
-================
+=============
 Deploy Bentos
-================
+=============
 
 Deploying a machine learning model can be quite a task. However, BentoCloud simplifies the process by enabling you to serve your model as an online service or on-demand function. This guide will walk you through how to deploy a Bento on BentoCloud.
 
-Deployment Mode
+Deployment mode
 ===============
 
 There are two Deployment modes on BentoCloud:
 
---------------
 Online Service
 --------------
 
 The Online Service Deployment offered by BentoCloud is ideal for low-latency serving scenarios. To ensure requests can be promptly addressed, instances in this mode are never scaled down to zero, maintaining a ready state for immediate processing. Furthermore, requests are directly routed to the serving instances of the API Server and Runners, bypassing any queuing mechanisms. This direct routing mechanism ensures minimum latency, providing an efficient and swift response to incoming requests.
 
-------------------
 On-Demand Function
 ------------------
 
 The On-Demand Function Deployment offered by BentoCloud is particularly suited for situations that prioritize cost-efficiency and reliability. In scenarios where requests are sporadic, this mode enables instances to scale down to zero, thereby conserving resources. This feature proves particularly beneficial for GPU-accelerated instances, which are generally more expensive to maintain. To ensure maximum reliability, especially during periods of cold-start or overload, requests are queued prior to processing. This mechanism enables the system to handle bursts of requests effectively, thus enhancing the robustness and dependability of your application under varying load conditions.
 
-Building Your Bento
-===================
+Build your Bento
+================
 
 1. To build your machine learning application into a Bento, check out this :doc:`/concepts/bento` in BentoML’s doc.
 2. To push your Bento to BentoCloud, do ``bentoml push <name>:<tag>``.  See :doc:`manage-models-and-bentos` for more details.
 
-Managing Deployments with the BentoCloud Console
-================================================
+Manage Deployments with the BentoCloud Console
+==============================================
 
 The BentoCloud Console enables you to perform basic management tasks with your Bentos using a browser.
 
---------------------
-Deploying Your Bento
---------------------
+Deploy your Bento
+-----------------
 
 1. Navigate to the **Deployments** section on BentoCloud and click the **Create** button in the upper-right corner.
 2. Choose the Deployment type (**Online Service** or **On-Demand Function**).
@@ -49,9 +46,8 @@ Deploying Your Bento
 
 Under the hood, the Bento is being built into an OCI Image to be deployed in BentoCloud. The deployment might take a few minutes, depending on your configuration.
 
------------------------
-Viewing Your Deployment
------------------------
+View your Deployment
+--------------------
 
 After your Bento is deployed, do the following to check the status of the Deployment:
 
@@ -62,9 +58,8 @@ After your Bento is deployed, do the following to check the status of the Deploy
 
 3. To update the Deployment, click **Update** in the upper-right corner, update your desired field, and click **Submit**.
 
----------------------------
-Terminating Your Deployment
----------------------------
+Terminate your Deployment
+-------------------------
 
 You can temporarily stop a Bento Deployment to make its endpoint inaccessible. The terminated Deployment can be restarted later and all the revision records are preserved.
 
@@ -76,9 +71,8 @@ To terminate a Deployment, do the following:
 
 You can restart the Deployment to make it available again by clicking **Restore**.
 
-------------------------
-Deleting Your Deployment
-------------------------
+Delete your Deployment
+----------------------
 
 After a Deployment is terminated, you can delete it. All the revision records will be deleted as well.
 
@@ -92,15 +86,15 @@ To delete a Deployment, do the following:
 
    You can't recover a Deployment after deleting it. This action is irreversible.
 
-Managing Deployments with the BentoML CLI
+Manage Deployments with the BentoML CLI
 =========================================
 
 The BentoML CLI is a set of tools that you can use to deploy any machine learning models as production-ready API endpoints on the cloud.
 To create and manage your Bento Deployments on BentoCloud, use ``bentoml deployment`` with the corresponding options.
 
---------------------
-Deploying Your Bento
---------------------
+Deploy your Bento
+-----------------
+
 Currently, the BentoML CLI only supports creating and updating a Bento Deployment by specifying a JSON file, which contains detailed configurations of the Deployment, such as ``name``, ``mode``, and ``targets``.
 The JSON file follows the same syntax as the **JSON** tab when you create or update a Deployment on the BentoCloud Console.
 
@@ -137,9 +131,8 @@ Run the following command to deploy a Bento.
           }
           EOF
 
------------------------
-Viewing Your Deployment
------------------------
+View your Deployment
+--------------------
 
 Run the following command to view all the existing Deployments on BentoCloud:
 
@@ -186,9 +179,8 @@ Run the following command to update a Deployment.
           }
           EOF
 
----------------------------
-Terminating Your Deployment
----------------------------
+Terminate your Deployment
+-------------------------
 
 You can temporarily stop a Bento Deployment to make its endpoint inaccessible. The terminated Deployment can be restarted later and all the revision records are preserved.
 
@@ -198,9 +190,8 @@ Run the following command to terminate a Deployment.
 
    bentoml deployment terminate <deployment_name>
 
-------------------------
-Deleting Your Deployment
-------------------------
+Delete your Deployment
+----------------------
 
 After a Deployment is terminated, you can delete it. All the revision records will be deleted as well.
 
@@ -216,8 +207,8 @@ Run the following command to delete a Deployment.
 
 For more information about ``bentoml deployment``, see :doc:`/reference/cli`.
 
-Interacting with Your Deployment
-================================
+Interact with your Deployment
+=============================
 
 Now that your model is deployed, you can send requests to it. Here's an example of how to send a request to your deployed model using ``curl``:
 

docs/source/bentocloud/how-tos/index.rst

@@ -1,10 +1,10 @@
 =============
-How-to Guides
+How-to guides
 =============
 
 How-to guides take the reader through the steps required to solve a problem.
 
-They are recipes, directions to achieve a specific end result, and are wholly **goal-oriented**.
+They are recipes, directions to achieve a specific end result, and are wholly **goal-oriented**.
 
 * :doc:`deploy-bentos`
 * :doc:`manage-access-token`

docs/source/bentocloud/how-tos/manage-access-token.rst

@@ -1,5 +1,5 @@
 ====================
-Manage Access Tokens
+Manage access tokens
 ====================
 
 In BentoCloud, API tokens serve as a key method of authorization for two distinct scopes - **BentoCloud resources** and **Bento Deployments**.
@@ -24,8 +24,8 @@ This tutorial explains how to create and use API tokens in BentoCloud.
 
 .. _creating-an-api-token:
 
-Creating an API Token
-=====================
+Create an API token
+===================
 
 To create an API token, perform the following steps:
 
@@ -42,8 +42,8 @@ To create an API token, perform the following steps:
 5. Record the token. This is the only opportunity to record it.
 6. All available tokens appear on the **API Tokens** page. Click **Delete** if you no longer needs a token.
 
-Using the Developer Token
-=========================
+Use the Developer token
+=======================
 
 Interact with BentoCloud programmatically via the BentoML Command Line
 Interface (CLI). Log in using the following command.
@@ -70,8 +70,8 @@ To retrieve the current endpoint and API token locally, make sure you have insta
 
 After you log in, you should be able to manage BentoCloud resources. For more information on the CLI, see :doc:`Reference - CLI </reference/cli>`.
 
-Using the User Token
-====================
+Use the User token
+==================
 
 You can use User tokens to access Protected Bento Deployments.
 

docs/source/bentocloud/how-tos/manage-models-and-bentos.rst

@@ -1,5 +1,5 @@
 ========================
-Manage Models and Bentos
+Manage models and Bentos
 ========================
 
 BentoML provides both a local Model Store and a Bento Store to host your models and Bentos, respectively. Once you've pushed these entities to BentoCloud,
@@ -8,13 +8,13 @@ they are stored in remote repositories. This setup helps teams manage them in a
 This document explains how to pull and push models and Bentos.
 
 Prerequisites
-=============
+-------------
 
 - You have :doc:`installed BentoML </quickstarts/install-bentoml>`.
 - You have logged in to BentoCloud. See :doc:`Manage Access Tokens <manage-access-token>` to learn more.
 
 Pull and push models
-====================
+--------------------
 
 * **Pull a model**: To fetch a specific version of a model from BentoCloud, run:
 
@@ -34,7 +34,7 @@ The **Models** page displays all available models on BentoCloud.
    :alt: manage-models.gif
 
 Pull and push Bentos
-====================
+--------------------
 
 * **Pull a Bento**: To fetch a specific version of a Bento from BentoCloud, run:
 

docs/source/bentocloud/how-tos/manage-users.rst

@@ -1,5 +1,5 @@
 ============
-Manage Users
+Manage users
 ============
 
 You can add and delete BentoCloud users to control and customize access to your resources.

docs/source/bentocloud/how-tos/monitor-events.rst

@@ -1,12 +1,12 @@
-================
-Monitor Events
-================
+==============
+Monitor events
+==============
 
 In BentoCloud, events provide a historical view of activities that have occurred within your resources.
 You can use these events to track changes, monitor the state of your resources, and troubleshoot when issues arise.
 Events can be related to different resources such as Models, Bentos, Deployments, and Clusters.
 
-Types of Events
+Types of events
 ===============
 
 1. **Model:** These events involve activities related to the models you've stored in BentoCloud.
@@ -16,10 +16,10 @@ Types of Events
    updating a deployment, or deleting a deployment.
 4. **Cluster:** Cluster events represent changes in the configuration or state of your BentoCloud cluster.
 
-Monitoring Events
-=================
+Monitor events
+==============
 
-Via User Interface
+Via user interface
 ------------------
 
 The simplest way to monitor these events is through the BentoCloud UI.
@@ -34,8 +34,8 @@ Via API
 For more advanced monitoring or to integrate event tracking with your own systems,
 you can use the BentoCloud API. This allows you to programmatically fetch events and process them according to your needs.
 
-Searching and Filtering Events
-==============================
+Search and filter events
+========================
 
 BentoCloud provides powerful tools for searching and filtering events.
 You can filter events by date, resource type, operation name, and creator.

docs/source/bentocloud/topics/index.rst

@@ -1,7 +1,8 @@
 ============
-Topic Guides
+Topic guides
 ============
-Topic Guides Discusses key topics and concepts at a fairly high level and provide useful background information and explanation.
+
+This section includes key topics and concepts at a fairly high level and provide useful background information and explanation.
 
 * :doc:`observability`
 

docs/source/bentocloud/topics/observability.rst

@@ -2,10 +2,10 @@ Observability
 ================
 
 
-DevOps Observability
+DevOps observability
 --------------------
 
-DevOps Observability encapsulates **metrics**, **logging**, and **tracing**.
+DevOps observability encapsulates **metrics**, **logging**, and **tracing**.
 BentoCloud automatically collects and visualizes all engineering **metrics** related to the deployment within the ``Dashboard`` tab in your respective ``Deployment`` page.
 Additionally, a **built-in log viewer** lets you examine the logs of your app and its infrastructure.
 These **logs** can be queried, downloaded, or routed into downstream log processing frameworks for more in-depth analysis.
@@ -15,7 +15,7 @@ For Enterprise Users, you will have access to a dedicated Grafana service where
 
 .. image:: ../../_static/img/bentocloud/grafana.png
 
-ML Observability
+ML observability
 ----------------
 
 As Machine Learning applications continue to proliferate and democratize, a new concept has emerged: ML Observability. This involves tracking the evolution of input data and model quality over time, to identify potential **feature drift** and **model drift**. For a deeper understanding of ML Monitoring, see `A Guide To ML Monitoring And Drift Detection <https://bentoml.com/blog/a-guide-to-ml-monitoring-and-drift-detection>`__.
@@ -27,7 +27,7 @@ BentoCloud enables you to conveniently configure your ML Monitoring Data for shi
 
 For detailed instructions on establishing ML Observability with BentoCloud, refer to our :ref:`monitoring-data-collectors-plugins`
 
-Integration with Ops Tooling
+Integration with Ops tooling
 ----------------------------
 
 Whether it's DevOps, DataOps, or MLOps, they all share a core principle in software engineering. These methodologies focus on unifying software development (Dev) and software operation (Ops), improving collaboration and communication, and automating processes to accelerate software delivery.