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Oreilly - Distributed Machine Learning Patterns, Video Edition
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Practical patterns for scaling machine learning from your laptop to a distributed cluster.
Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges of scaling machine learning systems.
In Distributed Machine Learning Patterns you will learn how to
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FILE LIST
Filename
Size
~Get Your Files Here !/001. Part 1. Basic concepts and background.mp4
2.4 MB
~Get Your Files Here !/002. Chapter 1. Introduction to distributed machine learning systems.mp4
27.9 MB
~Get Your Files Here !/003. Chapter 1. Distributed systems.mp4
7.2 MB
~Get Your Files Here !/004. Chapter 1. Distributed machine learning systems.mp4
13.2 MB
~Get Your Files Here !/005. Chapter 1. What we will learn in this book.mp4
7.6 MB
~Get Your Files Here !/006. Chapter 1. Summary.mp4
2.1 MB
~Get Your Files Here !/007. Part 2. Patterns of distributed machine learning systems.mp4
9.6 MB
~Get Your Files Here !/008. Chapter 2. Data ingestion patterns.mp4
14.8 MB
~Get Your Files Here !/009. Chapter 2. The Fashion-MNIST dataset.mp4
13.1 MB
~Get Your Files Here !/010. Chapter 2. Batching pattern.mp4
29.3 MB
~Get Your Files Here !/011. Chapter 2. Sharding pattern Splitting extremely large datasets among multiple machines.mp4
32.2 MB
~Get Your Files Here !/012. Chapter 2. Caching pattern.mp4
25.1 MB
~Get Your Files Here !/013. Chapter 2. Answers to exercises.mp4
1.2 MB
~Get Your Files Here !/014. Chapter 2. Summary.mp4
2.2 MB
~Get Your Files Here !/015. Chapter 3. Distributed training patterns.mp4
13 MB
~Get Your Files Here !/016. Chapter 3. Parameter server pattern Tagging entities in 8 million YouTube videos.mp4
39.5 MB
~Get Your Files Here !/017. Chapter 3. Collective communication pattern.mp4
36 MB
~Get Your Files Here !/018. Chapter 3. Elasticity and fault-tolerance pattern.mp4
27.1 MB
~Get Your Files Here !/019. Chapter 3. Answers to exercises.mp4
2.1 MB
~Get Your Files Here !/020. Chapter 3. Summary.mp4
1.9 MB
~Get Your Files Here !/021. Chapter 4. Model serving patterns.mp4
12.6 MB
~Get Your Files Here !/022. Chapter 4. Replicated services pattern Handling the growing number of serving requests.mp4
28.5 MB
~Get Your Files Here !/023. Chapter 4. Sharded services pattern.mp4
27.3 MB
~Get Your Files Here !/024. Chapter 4. The event-driven processing pattern.mp4
50.5 MB
~Get Your Files Here !/025. Chapter 4. Answers to exercises.mp4
1.8 MB
~Get Your Files Here !/026. Chapter 4. Summary.mp4
2.7 MB
~Get Your Files Here !/027. Chapter 5. Workflow patterns.mp4
18.8 MB
~Get Your Files Here !/028. Chapter 5. Fan-in and fan-out patterns Composing complex machine learning workflows.mp4
34.1 MB
~Get Your Files Here !/029. Chapter 5. Synchronous and asynchronous patterns Accelerating workflows with concurrency.mp4
25.4 MB
~Get Your Files Here !/030. Chapter 5. Step memoization pattern Skipping redundant workloads via memoized steps.mp4
28.2 MB
~Get Your Files Here !/031. Chapter 5. Answers to exercises.mp4
6.6 MB
~Get Your Files Here !/032. Chapter 5. Summary.mp4
2 MB
~Get Your Files Here !/033. Chapter 6. Operation patterns.mp4
18.1 MB
~Get Your Files Here !/034. Chapter 6. Scheduling patterns Assigning resources effectively in a shared cluster.mp4
49.7 MB
~Get Your Files Here !/035. Chapter 6. Metadata pattern Handle failures appropriately to minimize the negative effect on users.mp4
31.4 MB
~Get Your Files Here !/036. Chapter 6. Answers to exercises.mp4
2.6 MB
~Get Your Files Here !/037. Chapter 6. Summary.mp4
1.4 MB
~Get Your Files Here !/038. Part 3. Building a distributed machine learning workflow.mp4
4.2 MB
~Get Your Files Here !/039. Chapter 7. Project overview and system architecture.mp4
18.1 MB
~Get Your Files Here !/040. Chapter 7. Data ingestion.mp4
20.7 MB
~Get Your Files Here !/041. Chapter 7. Model training.mp4
13.8 MB
~Get Your Files Here !/042. Chapter 7. Model serving.mp4
10.2 MB
~Get Your Files Here !/043. Chapter 7. End-to-end workflow.mp4
20.3 MB
~Get Your Files Here !/044. Chapter 7. Answers to exercises.mp4
965.4 KB
~Get Your Files Here !/045. Chapter 7. Summary.mp4
2.1 MB
~Get Your Files Here !/046. Chapter 8. Overview of relevant technologies.mp4
25.6 MB
~Get Your Files Here !/047. Chapter 8. Kubernetes The distributed container orchestration system.mp4
18.5 MB
~Get Your Files Here !/048. Chapter 8. Kubeflow Machine learning workloads on Kubernetes.mp4
24.3 MB
~Get Your Files Here !/049. Chapter 8. Argo Workflows Container-native workflow engine.mp4
25.3 MB
~Get Your Files Here !/050. Chapter 8. Answers to exercises.mp4
1.3 MB
~Get Your Files Here !/051. Chapter 8. Summary.mp4
1.2 MB
~Get Your Files Here !/052. Chapter 9. A complete implementation.mp4
24.3 MB
~Get Your Files Here !/053. Chapter 9. Model training.mp4
34.7 MB
~Get Your Files Here !/054. Chapter 9. Model serving.mp4
20.5 MB
~Get Your Files Here !/055. Chapter 9. The end-to-end workflow.mp4
23.3 MB
~Get Your Files Here !/056. Chapter 9. Summary.mp4
