doc: Add content for V3 training, inference, model customization, ml_ops and sagemaker core (#5446)

* feat: Add basic V3 documentation structure and configuration

- Add ReadTheDocs configuration with sphinx-book-theme
- Create comprehensive overview, installation, and quickstart pages
- Set up documentation structure for all core capabilities
- Add custom CSS and Sphinx configuration
- Create symlinks to v3-examples and sagemaker-core directories
- Add docs/_build/ to .gitignore to exclude build artifacts

* feat: Add comprehensive training, inference, and model customization content

- Add detailed model customization with SFTTrainer, DPOTrainer, RLAIFTrainer, RLVRTrainer
- Include unified ModelTrainer interface for training workflows
- Add ModelBuilder for streamlined inference deployment
- Cover LoRA, full fine-tuning, and advanced ML techniques
- Include migration guides and production-ready examples

* feat: Add ML Ops and SageMaker Core documentation

- Add comprehensive MLOps pipeline orchestration and monitoring
- Include SageMaker Core low-level API documentation
- Cover advanced pipeline features and governance capabilities

---------

Co-authored-by: adishaa <adishaa@amazon.com>

Author: Aditi2424

docs/inference/index.rst

@@ -1,6 +1,186 @@
 Inference
 =========
 
-Inference documentation for SageMaker Python SDK V3.
+SageMaker Python SDK V3 transforms model deployment and inference with the unified **ModelBuilder** class, replacing the complex framework-specific model classes from V2. This modern approach provides a consistent interface for all inference scenarios while maintaining the flexibility and performance you need.
 
-Coming soon...
+Key Benefits of V3 Inference
+----------------------------
+
+* **Unified Interface**: Single ``ModelBuilder`` class replaces multiple framework-specific model classes
+* **Simplified Deployment**: Object-oriented API with intelligent defaults for endpoint configuration
+* **Enhanced Performance**: Optimized inference pipelines with automatic scaling and load balancing
+* **Multi-Modal Support**: Deploy models for real-time, batch, and serverless inference scenarios
+
+Quick Start Example
+-------------------
+
+Here's how inference has evolved from V2 to V3:
+
+**SageMaker Python SDK V2:**
+
+.. code-block:: python
+
+   from sagemaker.model import Model
+   from sagemaker.predictor import Predictor
+   
+   model = Model(
+       image_uri="my-inference-image",
+       model_data="s3://my-bucket/model.tar.gz",
+       role="arn:aws:iam::123456789012:role/SageMakerRole"
+   )
+   predictor = model.deploy(
+       initial_instance_count=1,
+       instance_type="ml.m5.xlarge"
+   )
+   result = predictor.predict(data)
+
+**SageMaker Python SDK V3:**
+
+.. code-block:: python
+
+   from sagemaker.serve import ModelBuilder
+   
+   model_builder = ModelBuilder(
+       model="my-model",
+       model_path="s3://my-bucket/model.tar.gz"
+   )
+   endpoint = model_builder.build()
+   result = endpoint.invoke(data)
+
+ModelBuilder Overview
+--------------------
+
+The ``ModelBuilder`` class is the cornerstone of SageMaker Python SDK V3 inference, providing a unified interface for all deployment scenarios. This single class replaces the complex web of framework-specific model classes from V2, offering:
+
+**Unified Deployment Interface**
+  One class handles PyTorch, TensorFlow, Scikit-learn, XGBoost, HuggingFace, and custom containers
+
+**Intelligent Optimization**
+  Automatically optimizes model serving configuration based on your model characteristics
+
+**Flexible Deployment Options**
+  Support for real-time endpoints, batch transform, and serverless inference
+
+**Seamless Integration**
+  Works seamlessly with SageMaker features like auto-scaling, multi-model endpoints, and A/B testing
+
+.. code-block:: python
+
+   from sagemaker.serve import ModelBuilder
+   from sagemaker.serve.configs import EndpointConfig
+
+   # Create model builder with intelligent defaults
+   model_builder = ModelBuilder(
+       model="your-model",
+       model_path="s3://your-bucket/model-artifacts",
+       role="your-sagemaker-role"
+   )
+
+   # Configure endpoint settings
+   endpoint_config = EndpointConfig(
+       instance_type="ml.m5.xlarge",
+       initial_instance_count=1,
+       auto_scaling_enabled=True
+   )
+
+   # Deploy model
+   endpoint = model_builder.build(endpoint_config=endpoint_config)
+   
+   # Make predictions
+   response = endpoint.invoke({"inputs": "your-input-data"})
+
+Inference Capabilities
+----------------------
+
+Model Optimization Support
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+V3 introduces powerful model optimization capabilities for enhanced performance:
+
+* **SageMaker Neo** - Optimize models for specific hardware targets
+* **TensorRT Integration** - Accelerate deep learning inference on NVIDIA GPUs
+* **ONNX Runtime** - Cross-platform model optimization and acceleration
+* **Quantization Support** - Reduce model size and improve inference speed
+
+**Quick Optimization Example:**
+
+.. code-block:: python
+
+   from sagemaker.serve import ModelBuilder
+   from sagemaker.serve.configs import OptimizationConfig
+
+   model_builder = ModelBuilder(
+       model="huggingface-bert-base",
+       optimization_config=OptimizationConfig(
+           target_device="ml_inf1",
+           optimization_level="O2",
+           quantization_enabled=True
+       )
+   )
+
+   optimized_endpoint = model_builder.build()
+
+Key Inference Features
+~~~~~~~~~~~~~~~~~~~~~
+
+* **Multi-Model Endpoints** - Host multiple models on a single endpoint with automatic model loading and unloading for cost optimization
+* **Auto-Scaling Integration** - Automatically scale endpoint capacity based on traffic patterns with configurable scaling policies
+* **A/B Testing Support** - Deploy multiple model variants with traffic splitting for safe model updates and performance comparison
+* **Batch Transform Jobs** - Process large datasets efficiently with automatic data partitioning and parallel processing
+* **Serverless Inference** - Pay-per-request pricing with automatic scaling from zero to handle variable workloads
+
+Supported Inference Scenarios
+-----------------------------
+
+Deployment Types
+~~~~~~~~~~~~~~~
+
+* **Real-Time Endpoints** - Low-latency inference for interactive applications
+* **Batch Transform** - High-throughput processing for large datasets
+* **Serverless Inference** - Cost-effective inference for variable workloads
+* **Multi-Model Endpoints** - Host multiple models on shared infrastructure
+
+Framework Support
+~~~~~~~~~~~~~~~~~
+
+* **PyTorch** - Deep learning models with dynamic computation graphs
+* **TensorFlow** - Production-ready machine learning models at scale
+* **Scikit-learn** - Classical machine learning algorithms
+* **XGBoost** - Gradient boosting models for structured data
+* **HuggingFace** - Pre-trained transformer models for NLP tasks
+* **Custom Containers** - Bring your own inference logic and dependencies
+
+Advanced Features
+~~~~~~~~~~~~~~~~
+
+* **Model Monitoring** - Track model performance and data drift in production
+* **Endpoint Security** - VPC support, encryption, and IAM-based access control
+* **Multi-AZ Deployment** - High availability with automatic failover
+* **Custom Inference Logic** - Implement preprocessing, postprocessing, and custom prediction logic
+
+Migration from V2
+------------------
+
+If you're migrating from V2, the key changes are:
+
+* Replace framework-specific model classes (PyTorchModel, TensorFlowModel, etc.) with ``ModelBuilder``
+* Use structured configuration objects instead of parameter dictionaries
+* Leverage the new ``invoke()`` method instead of ``predict()`` for more consistent API
+* Take advantage of built-in optimization and auto-scaling features
+
+Inference Examples
+-----------------
+
+Explore comprehensive inference examples that demonstrate V3 capabilities:
+
+.. toctree::
+   :maxdepth: 1
+
+   ../v3-examples/inference-examples/inference-spec-example
+   ../v3-examples/inference-examples/jumpstart-example
+   ../v3-examples/inference-examples/optimize-example
+   ../v3-examples/inference-examples/train-inference-e2e-example
+   ../v3-examples/inference-examples/jumpstart-e2e-training-example
+   ../v3-examples/inference-examples/local-mode-example
+   ../v3-examples/inference-examples/huggingface-example
+   ../v3-examples/inference-examples/in-process-mode-example