Oreilly – Designing Deep Learning Systems, Video Edition 2023-6

Designing Deep Learning Systems Video Edition course. In this training course, you will learn how to design and implement deep learning systems. These systems provide the necessary platforms and infrastructure to support deep learning models in production environments. This course is specifically designed for software engineers who have little knowledge of deep learning design requirements. The course is full of practical examples that will help you transfer your software development skills to building these deep learning platforms. You will learn how to build automated and scalable services for core tasks such as dataset management, model training and rendering, and hyperparameter tuning. This course is a great way to enter an exciting and rewarding career as a deep learning engineer.

What you will learn

• Transfer your software development skills to deep learning systems
• Identify and solve common engineering challenges for deep learning systems
• Understanding the deep learning development cycle
• Automating training for models in TensorFlow and PyTorch
• Optimizing dataset management, training, model presentation and hyperparameter tuning
• Choosing the right open source project for your platform

This course is suitable for people who:

• Software engineers with little knowledge of deep learning
• Interested in learning how to design and implement deep learning systems
• People looking to enter a high-paying career as a deep learning engineer

Designing Deep Learning Systems Video Edition Course Characteristics

• Publisher: Oreilly
• Instructor: Chi Wang , Donald Szeto
• Training level: beginner to advanced
• Training duration: 11 hours 57 minutes

Course headings

• Chapter 1. An introduction to deep learning systems
• Chapter 1. Deep learning system design overview
• Chapter 1. Building a deep learning system vs. developing a model
• Chapter 1. Summary
• Chapter 2. Dataset management service
• Chapter 2. Touring a sample dataset management service
• Chapter 2. Open source approaches
• Chapter 2. Summary
• Chapter 3. Model training service
• Chapter 3. Deep learning training code pattern
• Chapter 3. A sample model training service
• Chapter 3. Kubeflow training operators: An open source approach
• Chapter 3. When to use the public cloud
• Chapter 3. Summary
• Chapter 4. Distributed training
• Chapter 4. Data parallelism
• Chapter 4. A sample service supporting data parallel–distributed training
• Chapter 4. Training large models that can’t load on one GPU
• Chapter 4. Summary
• Chapter 5. Hyperparameter optimization service
• Chapter 5. Understanding hyperparameter optimization
• Chapter 5. Designing an HPO service
• Chapter 5. Open source HPO libraries
• Chapter 5. Summary
• Chapter 6. Model serving design
• Chapter 6. Common model serving strategies
• Chapter 6. Designing a prediction service
• Chapter 6. Summary
• Chapter 7. Model serving in practice
• Chapter 7. TorchServe model server sample
• Chapter 7. Model server vs. model service
• Chapter 7. Touring open source model serving tools
• Chapter 7. Releasing models
• Chapter 7. Postproduction model monitoring
• Chapter 7. Summary
• Chapter 8. Metadata and artifact store
• Chapter 8. Metadata in a deep learning context
• Chapter 8. Designing a metadata and artifacts store
• Chapter 8. Open source solutions
• Chapter 8. Summary
• Chapter 9. Workflow orchestration
• Chapter 9. Designing a workflow orchestration system
• Chapter 9. Touring open source workflow orchestration systems
• Chapter 9. Summary
• Chapter 10. Path to production
• Chapter 10. Model productionization
• Chapter 10. Model deployment strategies
• Chapter 10. Summary
• Appendix A. A “hello world” deep learning system
• Appendix A. Lab demo
• Appendix B. Survey of existing solutions
• Appendix B. Google Vertex AI
• Appendix B. Microsoft Azure Machine Learning
• Appendix B. Kubeflow
• Appendix B. Side-by-side comparison
• Appendix C. Creating an HPO service with Kubeflow Katib
• Appendix C. Getting started with Katib
• Appendix C. Expedite HPO
• Appendix C. Katib system design
• Appendix C. Adding a new algorithm
• Appendix C. Further reading
• Appendix C. When to use it

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