Overview
========

.. note::
   SageMaker Python SDK V3 contains breaking changes from V2. See the `V3.0.0 release notes <https://github.com/aws/sagemaker-python-sdk/releases/tag/v3.0.0>`_ for release notes or visit the `V2 documentation <https://sagemaker.readthedocs.io/en/v2/>`_.

SageMaker Python SDK v3.0 introduces a modern, modular API for training, fine-tuning, deploying, and managing models on Amazon SageMaker. This release replaces legacy interfaces such as Estimator, Model, and Predictor with unified classes like ModelTrainer and ModelBuilder, reducing boilerplate and simplifying workflows. V3 also introduces comprehensive fine-tuning support with new trainer classes for foundation models.

What's New in V3
-----------------

.. raw:: html

   <div class="whats-new-container">
     <div class="new-feature-card">
       <h3>Model Customization (V3 Exclusive)</h3>
       <p>Revolutionary foundation model fine-tuning with specialized trainers:</p>
       <ul>
         <li><strong>SFTTrainer</strong> - Supervised fine-tuning for task-specific adaptation</li>
         <li><strong>DPOTrainer</strong> - Direct preference optimization without RL complexity</li>
         <li><strong>RLAIFTrainer</strong> - Reinforcement learning from AI feedback</li>
         <li><strong>RLVRTrainer</strong> - Reinforcement learning from verifiable rewards</li>
       </ul>
       <p><em>Advanced techniques like LoRA, preference optimization, and RLHF that simply don't exist in V2.</em></p>
     </div>

     <div class="new-feature-card">
       <h3>Modular Architecture</h3>
       <p>Separate PyPI packages for specialized capabilities:</p>
       <ul>
         <li><strong>sagemaker-core</strong> - Low-level SageMaker resource management</li>
         <li><strong>sagemaker-train</strong> - Unified training with ModelTrainer</li>
         <li><strong>sagemaker-serve</strong> - Simplified inference with ModelBuilder</li>
         <li><strong>sagemaker-mlops</strong> - ML operations and pipeline management</li>
       </ul>
       <p>Single classes replace multiple framework-specific implementations:</p>
       <ul>
        <li><strong>ModelTrainer</strong> replaces PyTorchEstimator, TensorFlowEstimator, SKLearnEstimator, etc.</li>
        <li><strong>ModelBuilder</strong> replaces PyTorchModel, TensorFlowModel, SKLearnModel, etc.</li>
       </ul>
     </div>
   </div>

Capabilities
==============

Training with ModelTrainer
---------------------------

Unified training interface replacing framework-specific estimators with intelligent defaults and streamlined workflows:

.. code-block:: python

   from sagemaker.train import ModelTrainer
   from sagemaker.train.configs import InputData

   trainer = ModelTrainer(
       training_image="your-training-image",
       role="your-sagemaker-role"
   )

   train_data = InputData(
       channel_name="training",
       data_source="s3://your-bucket/train-data"
   )

   training_job = trainer.train(input_data_config=[train_data])

:doc:`Learn more about Training <training/index>`

Inference with ModelBuilder
----------------------------

Simplified model deployment and inference with the ModelBuilder

.. code-block:: python

   from sagemaker.serve import ModelBuilder

   model_builder = ModelBuilder(
       model="your-model",
       model_path="s3://your-bucket/model-artifacts"
   )

   model = model_builder.build(model_name="my-model")
   endpoint = model_builder.deploy(
       endpoint_name="my-endpoint",
       instance_type="ml.m5.xlarge",
       initial_instance_count=1
   )
   result = endpoint.invoke(
       body={"inputs": "your-input-data"},
       content_type="application/json"
   )

:doc:`Learn more about Inference <inference/index>`

ML Operations
-------------

Comprehensive MLOps capabilities for building, deploying, and managing machine learning workflows at scale:

.. code-block:: python

   from sagemaker.mlops import Pipeline, TrainingStep, ModelStep

   pipeline = Pipeline(name="production-ml-pipeline")
   
   training_step = TrainingStep(
       name="train-model",
       training_config=TrainingConfig(
           algorithm_specification={
               "training_image": "your-training-image"
           }
       )
   )
   
   pipeline.add_step(training_step)

:doc:`Learn more about ML Operations <ml_ops/index>`

SageMaker Core
--------------

Low-level, object-oriented access to Amazon SageMaker resources with intelligent defaults and type safety:

.. code-block:: python

   from sagemaker.core.resources import TrainingJob

   training_job = TrainingJob.create(
       training_job_name="my-training-job",
       role_arn="arn:aws:iam::123456789012:role/SageMakerRole",
       input_data_config=[{
           "channel_name": "training",
           "data_source": "s3://my-bucket/train"
       }]
   )

:doc:`Learn more about SageMaker Core <sagemaker_core/index>`

Getting Started
===============

Installation
------------

:doc:`Install SageMaker Python SDK V3 <installation>` to get started

Migration from V2
------------------

Key changes when migrating from V2:

* Replace Estimator classes with ``ModelTrainer``
* Replace Model classes with ``ModelBuilder``
* Use structured config objects instead of parameter dictionaries

Next Steps
-----------

**Get Started**: Follow the :doc:`quickstart` guide for a hands-on introduction