| 001. Part 1. Basic concepts and background.mp4 | 2.4 MB | ||
| 002. Chapter 1. Introduction to distributed machine learning systems.mp4 | 27.9 MB | ||
| 003. Chapter 1. Distributed systems.mp4 | 7.2 MB | ||
| 004. Chapter 1. Distributed machine learning systems.mp4 | 13.2 MB | ||
| 005. Chapter 1. What we will learn in this book.mp4 | 7.6 MB | ||
| 006. Chapter 1. Summary.mp4 | 2.1 MB | ||
| 007. Part 2. Patterns of distributed machine learning systems.mp4 | 9.6 MB | ||
| 008. Chapter 2. Data ingestion patterns.mp4 | 14.8 MB | ||
| 009. Chapter 2. The Fashion-MNIST dataset.mp4 | 13.1 MB | ||
| 010. Chapter 2. Batching pattern.mp4 | 29.3 MB | ||
| 011. Chapter 2. Sharding pattern Splitting extremely large datasets among multiple machines.mp4 | 32.2 MB | ||
| 012. Chapter 2. Caching pattern.mp4 | 25.1 MB | ||
| 013. Chapter 2. Answers to exercises.mp4 | 1.2 MB | ||
| 014. Chapter 2. Summary.mp4 | 2.2 MB | ||
| 015. Chapter 3. Distributed training patterns.mp4 | 13 MB | ||
| 016. Chapter 3. Parameter server pattern Tagging entities in 8 million YouTube videos.mp4 | 39.5 MB | ||
| 017. Chapter 3. Collective communication pattern.mp4 | 36 MB | ||
| 018. Chapter 3. Elasticity and fault-tolerance pattern.mp4 | 27.1 MB | ||
| 019. Chapter 3. Answers to exercises.mp4 | 2.1 MB | ||
| 020. Chapter 3. Summary.mp4 | 1.9 MB | ||
| 021. Chapter 4. Model serving patterns.mp4 | 12.6 MB | ||
| 022. Chapter 4. Replicated services pattern Handling the growing number of serving requests.mp4 | 28.5 MB | ||
| 023. Chapter 4. Sharded services pattern.mp4 | 27.3 MB | ||
| 024. Chapter 4. The event-driven processing pattern.mp4 | 50.5 MB | ||
| 025. Chapter 4. Answers to exercises.mp4 | 1.8 MB | ||
| 026. Chapter 4. Summary.mp4 | 2.7 MB | ||
| 027. Chapter 5. Workflow patterns.mp4 | 18.8 MB | ||
| 028. Chapter 5. Fan-in and fan-out patterns Composing complex machine learning workflows.mp4 | 34.1 MB | ||
| 029. Chapter 5. Synchronous and asynchronous patterns Accelerating workflows with concurrency.mp4 | 25.4 MB | ||
| 030. Chapter 5. Step memoization pattern Skipping redundant workloads via memoized steps.mp4 | 28.2 MB | ||
| 031. Chapter 5. Answers to exercises.mp4 | 6.6 MB | ||
| 032. Chapter 5. Summary.mp4 | 2 MB | ||
| 033. Chapter 6. Operation patterns.mp4 | 18.1 MB | ||
| 034. Chapter 6. Scheduling patterns Assigning resources effectively in a shared cluster.mp4 | 49.7 MB | ||
| 035. Chapter 6. Metadata pattern Handle failures appropriately to minimize the negative effect on users.mp4 | 31.4 MB | ||
| 036. Chapter 6. Answers to exercises.mp4 | 2.6 MB | ||
| 037. Chapter 6. Summary.mp4 | 1.4 MB | ||
| 038. Part 3. Building a distributed machine learning workflow.mp4 | 4.2 MB | ||
| 039. Chapter 7. Project overview and system architecture.mp4 | 18.1 MB | ||
| 040. Chapter 7. Data ingestion.mp4 | 20.7 MB | ||
| 041. Chapter 7. Model training.mp4 | 13.8 MB | ||
| 042. Chapter 7. Model serving.mp4 | 10.2 MB | ||
| 043. Chapter 7. End-to-end workflow.mp4 | 20.3 MB | ||
| 044. Chapter 7. Answers to exercises.mp4 | 965.4 KB | ||
| 045. Chapter 7. Summary.mp4 | 2.1 MB | ||
| 046. Chapter 8. Overview of relevant technologies.mp4 | 25.6 MB | ||
| 047. Chapter 8. Kubernetes The distributed container orchestration system.mp4 | 18.5 MB | ||
| 048. Chapter 8. Kubeflow Machine learning workloads on Kubernetes.mp4 | 24.3 MB | ||
| 049. Chapter 8. Argo Workflows Container-native workflow engine.mp4 | 25.3 MB | ||
| 050. Chapter 8. Answers to exercises.mp4 | 1.3 MB | ||
| 051. Chapter 8. Summary.mp4 | 1.2 MB | ||
| 052. Chapter 9. A complete implementation.mp4 | 24.3 MB | ||
| 053. Chapter 9. Model training.mp4 | 34.7 MB | ||
| 054. Chapter 9. Model serving.mp4 | 20.5 MB | ||
| 055. Chapter 9. The end-to-end workflow.mp4 | 23.3 MB | ||
| 056. Chapter 9. Summary.mp4 | 3.2 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 58 total files | |||
Distributed Machine Learning Patterns, Video Edition
https://FreeCourseWeb.com
Released 1/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 6h 21m | Size: 942 MB
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
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 292.7 MB | freecoursewb | 5 years | 0 | 0 |
All Comments