دانلود کتاب ایجاد خطوط لوله یادگیری عمیق مقیاس پذیر در AWS – توسعه، آموزش، و استقرار مدل های یادگیری عمیق

With the exponential growth of data and the increasing demand for sophisticated AI applications, traditional on-premises infrastructure often falls short in providing the computational power and scalability required for modern deep learning workflows. Consequently, cloud-based deep learning is becoming increasingly important as organizations seek scalable and efficient solutions for their machine learning tasks. By leveraging the flexibility, scalability, and cost-effectiveness of cloud services, organizations can overcome hardware limitations and accelerate the development and deployment of advanced machine learning algorithms. Integrating PySpark, PyTorch, TensorFlow, and Apache Airflow with Amazon Web Services (AWS), including Amazon Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), offers powerful tools for building and deploying deep learning models at scale. AWS, as the most popular cloud service provider with the largest market share, provides a robust infrastructure for executing these tasks efficiently. This book is designed for data scientists, machine learning engineers, and AI practitioners interested in building scalable deep learning pipelines on AWS or seeking to enhance their skills in integrating cloud services with deep learning frameworks. It provides a comprehensive guide to constructing a deep learning pipeline on AWS using a combination of PySpark, PyTorch, TensorFlow, and Airflow. We illustrate the entire workflow, starting with data ingestion and preprocessing using S3 and PySpark and concluding with scheduling the model to run on Apache Airflow. Each stage of the pipeline is dissected, offering clear explanations, hands-on examples, and insights drawn from real-world applications. This pipeline offers several advantages. Firstly, it capitalizes on the scalability of PySpark for large-scale data processing and the compute capacity of EC2 instances for model training, ensuring efficient utilization of resources. Secondly, a cloud-based approach provides flexibility, allowing resource scaling based on workload demands and easy provisioning of resources. Thirdly, PySpark and Apache Airflow simplify data processing tasks with their distributed computing capabilities, enabling efficient preprocessing, transformation, and workflow orchestration. Fourthly, the large storage capacity of Amazon S3 facilitates uninterrupted data storage and retrieval, supporting the processing of massive datasets. Lastly, the separation of concerns among preprocessing (PySpark), model development, training, and evaluation (PyTorch, TensorFlow), model orchestration (Apache Airflow), storage (S3), and compute (EC2) simplifies the architecture and enhances modularity, facilitating streamlined workflow management. The book also examines JupyterLab and Databricks to further enhance the development and deployment process. JupyterLab provides an interactive computing environment ideal for data exploration and prototyping, while Databricks offers a collaborative platform for big data processing and advanced analytics. This integration of multiple cloud services and data science tools fosters an end-to-end workflow, promoting collaboration and efficiency across the deep learning pipeline. The book is divided into ten chapters. In Chapter 1, readers are introduced to the fundamentals of deep learning pipelines, emphasizing the importance of scalability in such frameworks. Additionally, an overview of AWS as a platform for scalability is provided, laying the foundation for subsequent chapters that delve deeper into setting up, configuring, and optimizing deep learning environments on the AWS cloud.