Distributed Machine Learning Patterns, Video Edition
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6:20:35
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001. Part 1. Basic concepts and background.mp4
01:04
002. Chapter 1. Introduction to distributed machine learning systems.mp4
09:50
003. Chapter 1. Distributed systems.mp4
03:30
004. Chapter 1. Distributed machine learning systems.mp4
05:54
005. Chapter 1. What we will learn in this book.mp4
03:04
006. Chapter 1. Summary.mp4
00:44
007. Part 2. Patterns of distributed machine learning systems.mp4
03:25
008. Chapter 2. Data ingestion patterns.mp4
05:38
009. Chapter 2. The Fashion-MNIST dataset.mp4
04:40
010. Chapter 2. Batching pattern.mp4
12:06
011. Chapter 2. Sharding pattern Splitting extremely large datasets among multiple machines.mp4
13:56
012. Chapter 2. Caching pattern.mp4
10:15
013. Chapter 2. Answers to exercises.mp4
00:43
014. Chapter 2. Summary.mp4
00:44
015. Chapter 3. Distributed training patterns.mp4
05:02
016. Chapter 3. Parameter server pattern Tagging entities in 8 million YouTube videos.mp4
14:42
017. Chapter 3. Collective communication pattern.mp4
15:46
018. Chapter 3. Elasticity and fault-tolerance pattern.mp4
09:52
019. Chapter 3. Answers to exercises.mp4
01:03
020. Chapter 3. Summary.mp4
00:49
021. Chapter 4. Model serving patterns.mp4
04:31
022. Chapter 4. Replicated services pattern Handling the growing number of serving requests.mp4
11:59
023. Chapter 4. Sharded services pattern.mp4
10:55
024. Chapter 4. The event-driven processing pattern.mp4
20:02
025. Chapter 4. Answers to exercises.mp4
00:58
026. Chapter 4. Summary.mp4
00:50
027. Chapter 5. Workflow patterns.mp4
07:38
028. Chapter 5. Fan-in and fan-out patterns Composing complex machine learning workflows.mp4
13:49
029. Chapter 5. Synchronous and asynchronous patterns Accelerating workflows with concurrency.mp4
10:22
030. Chapter 5. Step memoization pattern Skipping redundant workloads via memoized steps.mp4
11:08
031. Chapter 5. Answers to exercises.mp4
02:07
032. Chapter 5. Summary.mp4
00:41
033. Chapter 6. Operation patterns.mp4
06:18
034. Chapter 6. Scheduling patterns Assigning resources effectively in a shared cluster.mp4
20:47
035. Chapter 6. Metadata pattern Handle failures appropriately to minimize the negative effect on users.mp4
12:48
036. Chapter 6. Answers to exercises.mp4
01:06
037. Chapter 6. Summary.mp4
00:33
038. Part 3. Building a distributed machine learning workflow.mp4
01:40
039. Chapter 7. Project overview and system architecture.mp4
06:32
040. Chapter 7. Data ingestion.mp4
08:30
041. Chapter 7. Model training.mp4
05:54
042. Chapter 7. Model serving.mp4
04:26
043. Chapter 7. End-to-end workflow.mp4
07:35
044. Chapter 7. Answers to exercises.mp4
00:38
045. Chapter 7. Summary.mp4
00:43
046. Chapter 8. Overview of relevant technologies.mp4
12:55
047. Chapter 8. Kubernetes The distributed container orchestration system.mp4
07:57
048. Chapter 8. Kubeflow Machine learning workloads on Kubernetes.mp4
10:33
049. Chapter 8. Argo Workflows Container-native workflow engine.mp4
10:38
050. Chapter 8. Answers to exercises.mp4
00:49
051. Chapter 8. Summary.mp4
00:31
052. Chapter 9. A complete implementation.mp4
08:52
053. Chapter 9. Model training.mp4
13:24
054. Chapter 9. Model serving.mp4
09:11
055. Chapter 9. The end-to-end workflow.mp4
09:34
056. Chapter 9. Summary.mp4
00:54
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- language english
- Training sessions 56
- duration 6:20:35
- Release Date 2024/06/14
