> ## Documentation Index
> Fetch the complete documentation index at: https://www.truefoundry.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Create Your First Deployment

> Get started with TrueFoundry by deploying your first service, job, or LLM model on the platform.

TrueFoundry helps you to seamlessly manage the entire machine learning lifecycle, from experimentation to deployment and beyond. You can:

1. Kickstart your machine learning journey by launching a Jupyter Notebook to explore and experiment with your ideas.

   * [Launch a Notebook](/docs/launch-notebooks)
   * [Launch SSH Server and Connect with VS Code](/docs/launch-an-ssh-server)

2. Once your model is ready for training, execute a model training job from within the Notebook using the Python SDK. Or you can push your training code to a Github Repository and deploy directly from a public Github repository

   * [Deploy a Job from Github Repo](/docs/deploy-job-from-a-public-github-repository)
   * [Deploy a Job using Python SDK](/docs/deploy-job-using-python-sdk)

3. Seamlessly log your trained model to the TrueFoundry Model Registry, which is backed by a secure blob storage service like S3, GCS, or Azure Container.

   * [Log your trained model](/docs/ml-repo-quickstart)

4. Deploy the logged model as a:

   1. Real-time API Service: Deploy your model as a real-time API Service to serve predictions in real-time, either from a public Github repository or from a local-machine / notebook

      * [Deploy a Service from Github Repo](/docs/deploy-first-service#deploy-from-github)
      * [Deploy a Service using Python SDK](/docs/deploy-service-programatically)

   2. Batch Inference Service: Deploy your model for batch inference to process large datasets efficiently by deploying it as a Job

   3. Async Service: Handle requests asynchronously using a queue to store intermediate requests by deploying an Async Service
      * [Deploy an Async Service](/docs/deploy-your-first-asyncservice)

5. LLM Testing and Deployment: Evaluate and compare the performance of various LLMs using TrueFoundry's AI Gateway capabilities. Once you've selected the desired LLM, deploy it with ease using pre-configured settings

   * [Compare various LLM's using AI Gateway](/docs/ai-gateway/openai)
   * [Deploy a LLM](/docs/deploying-an-llm-model-from-the-model-catalogue)

6. LLM Finetuning: Leverage TrueFoundry's LLM finetuning capabilities to tailor LLMs to your specific needs and data.

   * [Finetune a LLM on your own data](/docs/finetuning-a-model-from-the-model-catalogue)

***