Some improvements to deep learning landing page (#10736)

Signed-off-by: chenmoneygithub <chen.qian@databricks.com>

docs/source/deep-learning/index.rst

@@ -1,45 +1,69 @@
 Deep Learning
 =============
 
-The realm of deep learning has witnessed an unprecedented surge, revolutionizing numerous sectors with its ability to 
-process vast amounts of data and capture intricate patterns. From the real-time object detection in autonomous vehicles 
-to the generation of art through Generative Adversarial Networks, and from natural language processing applications in 
+The realm of deep learning has witnessed an unprecedented surge, revolutionizing numerous sectors with its ability to
+process vast amounts of data and capture intricate patterns. From the real-time object detection in autonomous vehicles
+to the generation of art through Generative Adversarial Networks, and from natural language processing applications in
 chatbots to predictive analytics in e-commerce, deep learning models are at the forefront of today's AI-driven innovations.
 
-MLflow acknowledges the profound impact and complexity of deep learning. With a keen focus on the unique challenges 
-posed by deep learning workflows, such as iterative model training and hyperparameter tuning, MLflow introduces a robust 
-suite of tools specifically designed for these advanced models. MLflow helps to facilitate seamless model development, 
-ensuring reproducibility, and provides enhanced monitoring capabilities with the concept of 'steps' for recording metrics at 
-various training iterations, with integrated UI features that enable you to easily visualize the iterative improvements 
-of key metrics during training epochs. 
-
-Key Benefits:
--------------
-* **Iterative Model Training**: With the concept of 'steps', MLflow allows users to log metrics at various training iterations, offering a granular view of the model's progress.
-* **Reproducibility**: Ensure that every model training run can be replicated with the exact same conditions.
-* **Scalability**: Handle projects ranging from small-scale models to enterprise-level deployments with ease.
-* **Traceability**: Keep track of every detail, from hyperparameters to the final model output.
-
-Deep Autologging Integrations
+In the deep learning realm, libraries such as PyTorch, Keras, Tensorflow provide handy tools to build and train deep learning
+models. MLflow, on the other hand, targets the problem of experiment tracking in deep learning, including logging your
+experiment setup (learning rate, batch size, etc) along with training metrics (loss, accuracy, etc) and the model
+(architecture, weights, etc). MLflow provides native integrations with deep learning libraries, so you can plug MLflow
+into your existing deep learning workflow with minimal changes to your code, and view your experiments in the MLflow UI.
+
+Why MLflow for Deep Learning?
 -----------------------------
-One of the standout features of MLflow's deep learning support is its deep autologging integrations. These integrations automatically 
-capture and log intricate details during the training of deep learning models, ensuring that every nuance, from model parameters to 
-evaluation metrics, is meticulously recorded. This is especially prominent in frameworks like TensorFlow, PyTorch Lightning, base PyTorch, 
-and Keras, making the iterative training process more insightful and manageable.
+MLflow offers a list of features that power your deep learning workflows:
+
+* **Experiments Tracking**: MLflow tracks your deep learning experiments, including parameters, metrics, and models.
+  Your experiments will be stored in the MLflow server, so you can compare across different experiments and share them.
+* **Model Registry**: You can register your trained deep learning models in the MLflow server, so you can easily
+  retrieve them later for inference.
+* **Model Deployment**: After training, you can serve the trained model with MLflow as a REST API endpoint, so you can
+  easily integrate it with your application.
+
+Experiments Tracking
+^^^^^^^^^^^^^^^^^^^^
+Tracking is the cornerstone of the MLflow ecosystem, and especially vital for the iterative nature of deep learning:
+
+- **Experiments and Runs**: Organize your deep learning projects into experiments, with each experiment containing multiple runs.
+  Each run captures essential data like metrics at various training steps, hyperparameters, and the code state.
+- **Artifacts**: Store vital outputs such as deep learning models, visualizations, or even tensorboard logs. This artifact
+  repository ensures traceability and easy access.
+- **Metrics at Steps**: With deep learning's iterative nature, MLflow allows logging metrics at various training steps,
+  offering a granular view of the model's progress.
+- **Dependencies and Environment**: Capture the computational environment, including deep learning frameworks' versions,
+  ensuring reproducibility.
+- **Input Examples and Model Signatures**: Define the expected format of the model's inputs, crucial for complex data like
+  images or sequences.
+- **UI Integration**: The enhanced UI provides a visual overview of deep learning runs, facilitating comparison and insights
+  into training progress.
+- **Search Functionality**: Efficiently navigate through your deep learning experiments using robust search capabilities.
+- **APIs**: Interact with the tracking system programmatically, integrating deep learning workflows seamlessly.
 
-Native Library Support
-----------------------
-Deep learning in MLflow is enriched by its native support for a number of the most popular libraries. The native integration with 
-each of these libraries within MLflow help to streamline and simplify the training process, as well as saving, logging, loading, and 
-representing models as generic Python functions for inference use anywhere. 
 
-Opting for these native integrations brings forth a myriad of advantages:
+Model Registry
+^^^^^^^^^^^^^^
+A centralized repository for your deep learning models:
 
-* **Auto-logging Capabilities**: Automatically capture details without manual intervention.
-* **Custom Serialization**: Streamline the model saving and loading process with custom methods tailored for each library.
-* **Unified Interface**: Regardless of the underlying library, interact with a consistent MLflow interface.
+- **Versioning**: Handle multiple iterations and versions of deep learning models, facilitating comparison or reversion.
+- **Annotations**: Attach notes, training datasets, or other relevant metadata to models.
+- **Lifecycle Stages**: Clearly define the stage of each model version, ensuring clarity in deployment and further fine-tuning.
+
+Model Deployment
+^^^^^^^^^^^^^^^^
+Transition deep learning models from training to real-world applications:
+
+- **Consistency**: Ensure models, especially those with GPU dependencies, behave consistently across different deployment environments.
+- **Docker and GPU Support**: Deploy in containerized environments, ensuring all dependencies, including GPU support, are encapsulated.
+- **Scalability**: From deploying a single model to serving multiple distributed deep learning models, MLflow scales as per
+  your requirements.
 
-The officially supported integrations for deep learning libraries in MLflow encompass:
+Native Library Support
+----------------------
+MLflow has native integrations with common deep learning libraries, such as PyTorch, Keras and Tensorflow, so you can plug
+MLflow into your workflow easily. The officially supported integrations for deep learning libraries in MLflow encompass:
 
 .. raw:: html
 
@@ -124,33 +148,3 @@ For detailed guide on how to integrate MLflow with these libraries, refer to the
     keras/index
     tensorflow/index
     pytorch/index
-
-MLflow Tracking for Deep Learning
----------------------------------
-Tracking remains a cornerstone of the MLflow ecosystem, especially vital for the iterative nature of deep learning:
-
-- **Experiments and Runs**: Organize your deep learning projects into experiments, with each experiment containing multiple runs. Each run captures essential data like metrics at various training steps, hyperparameters, and the code state.
-- **Artifacts**: Store vital outputs such as deep learning models, visualizations, or even tensorboard logs. This artifact repository ensures traceability and easy access.
-- **Metrics at Steps**: With deep learning's iterative nature, MLflow allows logging metrics at various training steps, offering a granular view of the model's progress.
-- **Dependencies and Environment**: Capture the computational environment, including deep learning frameworks' versions, ensuring reproducibility.
-- **Input Examples and Model Signatures**: Define the expected format of the model's inputs, crucial for complex data like images or sequences.
-- **UI Integration**: The enhanced UI provides a visual overview of deep learning runs, facilitating comparison and insights into training progress.
-- **Search Functionality**: Efficiently navigate through your deep learning experiments using robust search capabilities.
-- **APIs**: Interact with the tracking system programmatically, integrating deep learning workflows seamlessly.
-
-
-Model Registry
---------------
-A centralized repository for your deep learning models:
-
-- **Versioning**: Handle multiple iterations and versions of deep learning models, facilitating comparison or reversion.
-- **Annotations**: Attach notes, training datasets, or other relevant metadata to models.
-- **Lifecycle Stages**: Clearly define the stage of each model version, ensuring clarity in deployment and further fine-tuning.
-
-Deployment for Deep Learning Models
------------------------------------
-Transition deep learning models from training to real-world applications:
-
-- **Consistency**: Ensure models, especially those with GPU dependencies, behave consistently across different deployment environments.
-- **Docker and GPU Support**: Deploy in containerized environments, ensuring all dependencies, including GPU support, are encapsulated.
-- **Scalability**: From deploying a single model to serving multiple distributed deep learning models, MLflow scales as per your requirements.