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CodeWhisperer is powered by a Large Language Model (LLM) that is trained on billions of lines of code, and as a result, has learned how to write code in 15 programming languages. They were able to create a preprocessing data class just by typing “class to create preprocessing script for ML data.”
Our solution describes an AWS DeepRacer environment configuration using the AWS CDK to accelerate the journey of users experimenting with SageMaker log analysis and reinforcement learning on AWS for an AWS DeepRacer event. Choose Open Jupyter to start running the Python script for performing the log analysis.
Create a healthcare folder in the bucket you named via your AWS CDK script. He has over 8 years of industry experience from startups to large-scale enterprises, from IoT Research Engineer, Data Scientist, to Data & AI Architect. Then upload flow-healthcarediabetesunclean.csv to the folder and let the automation happen!
This post showcases how to have a repeatable process with low-code tools like Amazon SageMaker Autopilot such that it can be seamlessly integrated into your environment, so you don’t have to orchestrate this end-to-end workflow on your own. You can also add your own Python scripts and transformations to customize workflows.
If you have a different format, you can potentially use Llama convert scripts or Mistral convert scripts to convert your model to a supported format. In this section, we’ll show you how to fine-tune the Llama 3.2 The fine-tuning scripts are based on the scripts provided by the Llama fine-tuning repository.
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