Description
Book Synopsis: With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.
Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more. Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot. Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment. Tie everything together into a repeatable machine learning operations pipeline. Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka. Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more.
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Are you ready to take your data science projects to the next level? Look no further than "Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines." This practical book is your key to successfully building and deploying data science projects on Amazon Web Services (AWS). With the unified Amazon AI and machine learning stack, you can level up your skills in data science, data engineering, and application development.
Imagine being able to build and run pipelines in the cloud, and integrate the results into applications in minutes instead of days. Authors Chris Fregly and Antje Barth will show you exactly how to do that. By implementing the Amazon AI and ML stack, you can tackle real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more. The power of automated machine learning with SageMaker Autopilot will be at your fingertips for a specific subset of use cases.
Dive deep into the model development lifecycle as the authors guide you through a BERT-based NLP use case that includes data ingestion, analysis, model training, and deployment. With their expertise, you'll be able to tie everything together into a repeatable machine learning operations pipeline. Real-time ML, anomaly detection, and streaming analytics are also covered in the book, showcasing the capabilities of Amazon Kinesis and Managed Streaming for Apache Kafka.
At every step, the authors emphasize the importance of security best practices for data science projects and workflows. Learn about identity and access management, authentication, authorization, and more, ensuring that your projects are secure and compliant.
Don't miss out on the opportunity to boost your data science skills and revolutionize your projects with AWS. Get your copy of "Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines" today and start building innovative solutions that will transform your business.
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