Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

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Publisher ‏ : ‎ O’Reilly Media; 1st edition (May 11, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 521 pages
ISBN-10 ‏ : ‎ 1492079391
ISBN-13 ‏ : ‎ 978-1492079392
Item Weight ‏ : ‎ 1.82 pounds
Dimensions ‏ : ‎ 7 x 1.05 x 9.19 inches

Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

In the world of data science, implementing end-to-end, continuous AI and machine learning pipelines is essential for delivering accurate and timely insights. With the vast amount of data being generated every day, organizations need to leverage advanced tools and technologies to extract valuable information from their data.

One such tool that is widely used in the data science community is Amazon Web Services (AWS). AWS provides a comprehensive suite of services that enable data scientists to build, train, and deploy machine learning models at scale. By leveraging AWS, data scientists can streamline their workflow and focus on developing innovative solutions rather than managing infrastructure.

To implement end-to-end, continuous AI and machine learning pipelines on AWS, data scientists can follow these steps:

1. Data Collection: The first step in building a machine learning pipeline is to collect and store the data. AWS offers services like Amazon S3 and Amazon RDS for storing and managing large datasets.

2. Data Preprocessing: Once the data is collected, it needs to be cleaned and preprocessed before it can be used for training machine learning models. AWS provides services like Amazon SageMaker for data preprocessing and feature engineering.

3. Model Training: After the data is preprocessed, data scientists can train their machine learning models using AWS SageMaker. SageMaker offers built-in algorithms and tools for training models on large datasets.

4. Model Deployment: Once the model is trained, it needs to be deployed in a production environment. AWS provides services like Amazon SageMaker hosting for deploying machine learning models as RESTful APIs.

5. Continuous Integration and Deployment: To ensure that the machine learning pipeline is always up to date, data scientists can use AWS CodePipeline and AWS CodeBuild for continuous integration and deployment.

By following these steps and leveraging the power of AWS, data scientists can build end-to-end, continuous AI and machine learning pipelines that deliver valuable insights to organizations. With AWS’s scalable infrastructure and advanced tools, data scientists can focus on developing innovative solutions and driving business growth.
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