What is the way to implement LLM in azure machine learning

Question

The designer is not supported to develop a LLM nor implement a LLM based on my learning. if i want to deploy one LLM, how should i start my project?

Answer 1

@Long Wong

Thanks for reaching out to us, there are two ways you can think about for your project.

First is the model catalog feature in Azure Machine Learning studio, you can leverage the model directly in the Azure Machine Learning Studio as below screenshot -

Then while Azure Machine Learning (AML) doesn't have out-of-the-box support for LLM, you can still implement and deploy it using the Python SDK and the Azure Machine Learning resources. Here are the general steps:

1. Environment Setup: Create an Azure Machine Learning workspace in the Azure portal. Once the workspace is ready, you can create a new compute instance from the Studio.
2. Data Preparation: You will need a dataset for training. You can upload your data to Azure Blob Storage or Datastore and then access it from the Azure Machine Learning workspace.
3. Model Development: Develop your LLM using your preferred NLP library, such as Hugging Face's Transformers, in a Jupyter notebook in the compute instance you created. You will need to fine-tune a pre-trained language model on your specific task.
4. Experiment Tracking: Use the Azure Machine Learning Python SDK to log metrics and track your experiments. This will help you monitor the training process and select the best model.
5. Model Training: Train your LLM model using Azure Machine Learning's training capabilities. You can use Azure Machine Learning Pipelines to automate and manage your training process.
6. Model Deployment: Once your model is trained and you're satisfied with the results, you can deploy it as a web service using Azure Machine Learning.
7. Model Management and Monitoring: Monitor the performance of your deployed model using Azure Machine Learning's monitoring capabilities. You can also manage the model's lifecycle using the model registry. Note: Each of these steps can be complex and require a good understanding of both Azure Machine Learning and NLP. Make sure to refer to Azure Machine Learning's documentation and the documentation of the NLP library you're using for detailed guides and best practices.

I hope this helps.

Regards,

Yutong

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