Data scientists and machine learning (ML) developers use the
Agent Platform SDK for Python to build, train, and deploy models in a custom ML
workflow. This includes creating datasets and uploading data, training an ML
model, uploading and storing your model, deploying your model, running batch
prediction jobs, and managing your models and endpoints.

The Agent Platform SDK also includes classes to create generative AI
solutions with text, code, chat, and text embedding foundation models. You can
use these classes to generate text, create a text or code chatbot, tune a
foundation model, and create a text embedding. A text embedding is text in the
form of a vector used to search for items. For more information, see
[Agent Platform SDK language model classes](https://docs.cloud.google.com/python/docs/reference/aiplatform/latest/vertexai).

You can use the Agent Platform SDK for Python in hosted JupyterLab notebooks within
Gemini Enterprise Agent Platform to write and run your code. The notebooks include preinstalled
ML frameworks, such as TensorFlow and PyTorch. You can also use other notebooks,
such as Colab notebooks, or use a developer environment of your choice that
supports Python.

If you want to try using the Agent Platform SDK for Python right now, see the following
resources:

- [Introduction to the Agent Platform SDK for Python](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/python-sdk/use-python-sdk.md.txt)
- [Agent Platform SDK reference](https://docs.cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.md.txt)
- [Agent Platform SDK language model reference](https://docs.cloud.google.com/python/docs/reference/aiplatform/latest/vertexai)
- [Train a model using Gemini Enterprise Agent Platform and the Python SDK](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/tutorials/tabular-bq-prediction.md.txt)

The Agent Platform SDK includes many classes to help you automate data
ingestion, train models, and get predictions. It also includes classes to help
you monitor, evaluate, and optimize your machine learning (ML) workflow. The
classes can be loosely grouped into the following categories:

- [Data classes](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/python-sdk/data-classes.md.txt) include classes that work with structured data, unstructured data, and the Gemini Enterprise Agent Platform Feature Store.
- [Training classes](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/python-sdk/training-classes.md.txt) include classes that work with AutoML training for structured and unstructured data, custom training, hyperparameter training, and pipeline training.
- [Model classes](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/python-sdk/model-classes.md.txt) work with models and model evaluations.
- [Prediction classes](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/python-sdk/prediction-classes.md.txt) work with batch predictions, online predictions, and Vector Search predictions.
- [Tracking classes](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/python-sdk/tracking-classes.md.txt) work with Gemini Enterprise Agent Platform ML Metadata, Experiments on Agent Platform, and Vertex AI TensorBoard.