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Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. Next, focus on building the components of the architecture—such as training and preprocessing scripts—within SageMaker Studio or Jupyter Notebook.
Accenture is using Amazon CodeWhisperer to accelerate coding as part of our software engineering best practices initiative in our Velocity platform,” says Balakrishnan Viswanathan, Senior Manager, Tech Architecture at Accenture. In this post, we illustrate how Accenture uses CodeWhisperer in practice to improve developer productivity.
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.
Now we have low-code and no-code tools like Amazon SageMaker Data Wrangler , AWS Glue DataBrew , and Amazon SageMaker Canvas to assist with data feature engineering. However, a lot of these processes are still currently done manually by a data engineer or analyst who analyzes the data using these tools.
You can integrate a Data Wrangler data preparation flow into your ML workflows to simplify and streamline data preprocessing and feature engineering using little to no coding. You can also add your own Python scripts and transformations to customize workflows. Choose the object file in this folder to see the test results.
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. The fine-tuning scripts are based on the scripts provided by the Llama fine-tuning repository. degree in Electrical Engineering. from sagemaker.s3
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