Designing Deep Learning Systems, Video Edition
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11:56:56
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001. Chapter 1. An introduction to deep learning systems.mp4
35:11
002. Chapter 1. Deep learning system design overview.mp4
33:06
003. Chapter 1. Building a deep learning system vs. developing a model.mp4
01:20
004. Chapter 1. Summary.mp4
03:29
005. Chapter 2. Dataset management service.mp4
23:55
006. Chapter 2. Touring a sample dataset management service.mp4
44:12
007. Chapter 2. Open source approaches.mp4
20:35
008. Chapter 2. Summary.mp4
02:33
009. Chapter 3. Model training service.mp4
13:21
010. Chapter 3. Deep learning training code pattern.mp4
06:08
011. Chapter 3. A sample model training service.mp4
25:16
012. Chapter 3. Kubeflow training operators An open source approach.mp4
15:10
013. Chapter 3. When to use the public cloud.mp4
07:19
014. Chapter 3. Summary.mp4
01:39
015. Chapter 4. Distributed training.mp4
04:53
016. Chapter 4. Data parallelism.mp4
24:13
017. Chapter 4. A sample service supporting data paralleldistributed training.mp4
14:51
018. Chapter 4. Training large models that cant load on one GPU.mp4
17:54
019. Chapter 4. Summary.mp4
02:40
020. Chapter 5. Hyperparameter optimization service.mp4
08:14
021. Chapter 5. Understanding hyperparameter optimization.mp4
26:55
022. Chapter 5. Designing an HPO service.mp4
09:08
023. Chapter 5. Open source HPO libraries.mp4
15:43
024. Chapter 5. Summary.mp4
02:14
025. Chapter 6. Model serving design.mp4
19:11
026. Chapter 6. Common model serving strategies.mp4
08:07
027. Chapter 6. Designing a prediction service.mp4
25:50
028. Chapter 6. Summary.mp4
01:35
029. Chapter 7. Model serving in practice.mp4
26:06
030. Chapter 7. TorchServe model server sample.mp4
18:24
031. Chapter 7. Model server vs. model service.mp4
02:54
032. Chapter 7. Touring open source model serving tools.mp4
26:42
033. Chapter 7. Releasing models.mp4
12:44
034. Chapter 7. Postproduction model monitoring.mp4
05:41
035. Chapter 7. Summary.mp4
02:13
036. Chapter 8. Metadata and artifact store.mp4
04:42
037. Chapter 8. Metadata in a deep learning context.mp4
13:31
038. Chapter 8. Designing a metadata and artifacts store.mp4
10:32
039. Chapter 8. Open source solutions.mp4
11:32
040. Chapter 8. Summary.mp4
01:07
041. Chapter 9. Workflow orchestration.mp4
15:37
042. Chapter 9. Designing a workflow orchestration system.mp4
14:47
043. Chapter 9. Touring open source workflow orchestration systems.mp4
23:07
044. Chapter 9. Summary.mp4
01:20
045. Chapter 10. Path to production.mp4
13:15
046. Chapter 10. Model productionization.mp4
09:44
047. Chapter 10. Model deployment strategies.mp4
04:42
048. Chapter 10. Summary.mp4
01:25
049. Appendix A. A hello world deep learning system.mp4
08:28
050. Appendix A. Lab demo.mp4
10:09
051. Appendix B. Survey of existing solutions.mp4
07:34
052. Appendix B. Google Vertex AI.mp4
05:49
053. Appendix B. Microsoft Azure Machine Learning.mp4
05:49
054. Appendix B. Kubeflow.mp4
04:33
055. Appendix B. Side-by-side comparison.mp4
00:23
056. Appendix C. Creating an HPO service with Kubeflow Katib.mp4
03:45
057. Appendix C. Getting started with Katib.mp4
14:16
058. Appendix C. Expedite HPO.mp4
02:56
059. Appendix C. Katib system design.mp4
13:25
060. Appendix C. Adding a new algorithm.mp4
02:51
061. Appendix C. Further reading.mp4
00:53
062. Appendix C. When to use it.mp4
01:18
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- language english
- Training sessions 62
- duration 11:56:56
- Release Date 2024/05/19
