AWS Machine Learning Blog

NOVEMBER 19, 2024

This session covers the technical process, from data preparation to model customization techniques, training strategies, deployment considerations, and post-customization evaluation. Explore how this powerful tool streamlines the entire ML lifecycle, from data preparation to model deployment.

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AWS ML ML AI 108

DagsHub

JULY 25, 2024

TensorFlow The Google Brain team created the open-source deep learning framework TensorFlow, which was made available in 2015. A good understanding of Python and machine learning concepts is recommended to fully leverage TensorFlow's capabilities. Before using Keras, ensure you have a basic understanding of Python and neural networks.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

IBM Journey to AI blog

AUGUST 11, 2023

Because the machine learning lifecycle has many complex components that reach across multiple teams, it requires close-knit collaboration to ensure that hand-offs occur efficiently, from data preparation and model training to model deployment and monitoring.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

PyImageSearch

NOVEMBER 6, 2023

We also import the Image class from the PIL (Python Imaging Library) to handle image operations on Line 8. Lastly, on Line 10 , the tqdm library is incorporated to display progress bars during data processing and model training. Key steps encompass: Data preparation and splitting into training and validation sets.

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Deep Learning Deep Learning Python Computer Science 69

AWS Machine Learning Blog

NOVEMBER 11, 2024

We then also cover how to fine-tune the model using 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 models using the SageMaker Python SDK. You can access the Meta Llama 3.2

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AI AI ML ML 116