Remove 2012 Remove Data Preparation Remove Python
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Causal Inference Python Implementation

Towards AI

Photo by SHVETS production from Pexels As per the routine I follow every time, here I am with the Python implementation of Causal Impact. This historical sales data covers sales information from 2010–02–05 to 2012–11–01. So let’s filter out and keep only a handful of data to perform the analysis.

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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Flipboard

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity.

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Fine-tune multimodal models for vision and text use cases on Amazon SageMaker JumpStart

AWS Machine Learning Blog

We cover two approaches: using the Amazon SageMaker Studio UI for a no-code solution, and using the SageMaker Python SDK. FMs through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. Fine-tune using the SageMaker Python SDK You can also fine-tune Meta Llama 3.2 Vision models. WASHINGTON, D.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning Blog

With the introduction of EMR Serverless support for Apache Livy endpoints , SageMaker Studio users can now seamlessly integrate their Jupyter notebooks running sparkmagic kernels with the powerful data processing capabilities of EMR Serverless. elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*" elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*"

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Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning Blog

It serializes these configuration dictionaries (or config dict for short) to their ProtoBuf representation, transports them to the client using gRPC, and then deserializes them back to Python dictionaries. Details of the data preparation code are in the following notebook.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

If you are prompted to choose a kernel, choose Data Science as the image and Python 3 as the kernel, then choose Select. as the image and Glue Python [PySpark and Ray] as the kernel, then choose Select. The environment preparation process may take some time to complete.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code. or later image versions.

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