Price: $53.19
(as of Dec 27,2024 12:48:01 UTC – Details)
ASIN : B0BFFDF4MQ
Publisher : Packt Publishing; 1st edition (May 31, 2023)
Publication date : May 31, 2023
Language : English
File size : 12078 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 258 pages
Pretrain Vision and Large Language Models in Python: End-to-end techniques for building and deploying foundation models on AWS
In this post, we will explore the process of pretraining vision and large language models in Python using end-to-end techniques. We will cover the steps involved in building and deploying foundation models on AWS, a popular cloud computing platform.
Pretraining vision and language models has become an essential step in the development of cutting-edge AI applications. By pretraining these models on large datasets, we can leverage transfer learning to build more accurate and efficient models for a wide range of tasks.
To get started, we will walk through the process of setting up a development environment in Python, including installing the necessary libraries and tools. We will then explore how to pretrain vision and language models using popular frameworks such as PyTorch and TensorFlow.
Next, we will discuss best practices for fine-tuning pretrained models on custom datasets to improve performance and accuracy. We will cover techniques such as data augmentation, hyperparameter tuning, and model evaluation.
Finally, we will demonstrate how to deploy our pretrained models on AWS using services such as Amazon SageMaker. We will walk through the steps involved in packaging our model for deployment, setting up a hosting environment, and serving predictions to end users.
By the end of this post, you will have a solid understanding of how to pretrain vision and language models in Python and deploy them on AWS. Whether you are a seasoned AI practitioner or just getting started with machine learning, this post will provide you with the knowledge and tools you need to build and deploy state-of-the-art models for your own projects.
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