Deploying Scalable Machine Learning for Data Science磁力链接_Deploying Scalable Machine Learning for Data Sciencebt种子下载_Deploying Scalable Machine Learning for Data Science迅雷下载

Deploying Scalable Machine Learning for Data Science

文件类型	收录时间	最后活跃	资源热度	文件大小	文件数量
视频	2018-11-23 04:06	2024-12-1 22:45	146	177.79 MB	32

磁力链接

magnet:?xt=urn:btih:0421a839a023a8286fb1120649680d743a1073df

迅雷链接

thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjA0MjFhODM5YTAyM2E4Mjg2ZmIxMTIwNjQ5NjgwZDc0M2ExMDczZGZaWg==

二维码链接

Deploying Scalable Machine Learning for Data Science的二维码

种子下载(838888不存储任何种子文件)

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种子下载线路2(比特彗星)
种子下载线路3(torcache)
3条线路均为国内外知名下载网站种子链接，内容跟本站无关！

文件列表

5.4. Running ML Services in Containers/19.Example Docker build process.mp411.13MB
1.Introduction/01.Scaling ML models.mp43.21MB
1.Introduction/02.What you should know.mp42.52MB
2.1. The Need to Scale ML Models/03.Building and running ML models for data scientists.mp49.57MB
2.1. The Need to Scale ML Models/04.Building and deploying ML models for production use.mp47.67MB
2.1. The Need to Scale ML Models/05.Definition of scaling ML for production.mp47.15MB
2.1. The Need to Scale ML Models/06.Overview of tools and techniques for scalable ML.mp49.82MB
3.2. Design Patterns for Scalable ML Applications/07.Horizontal vs. vertical scaling.mp46.53MB
3.2. Design Patterns for Scalable ML Applications/08.Running models as services.mp42.99MB
3.2. Design Patterns for Scalable ML Applications/09.APIs for ML model services.mp48.33MB
3.2. Design Patterns for Scalable ML Applications/10.Load balancing and clusters of servers.mp46.57MB
3.2. Design Patterns for Scalable ML Applications/11.Scaling horizontally with containers.mp44.66MB
4.3. Deploying ML Models as Services/12.Services encapsulate ML models.mp44.45MB
4.3. Deploying ML Models as Services/13.Using Plumber to create APIs for R programs.mp46.43MB
4.3. Deploying ML Models as Services/14.Using Flask to create APIs for Python programs.mp48.51MB
4.3. Deploying ML Models as Services/15.Best practices for API design for ML services.mp41.99MB
5.4. Running ML Services in Containers/16.Containers bundle ML model components.mp46.67MB
5.4. Running ML Services in Containers/17.Introduction to Docker.mp45.93MB
5.4. Running ML Services in Containers/18.Building Docker images with Dockerfiles.mp47.68MB
5.4. Running ML Services in Containers/20.Using Docker registries to manage images.mp46.47MB
6.5. Scaling ML Services with Kubernetes/21.Running services in clusters.mp44.55MB
6.5. Scaling ML Services with Kubernetes/22.Introduction to Kubernetes.mp45.69MB
6.5. Scaling ML Services with Kubernetes/23.Creating a Kubernetes cluster.mp47.52MB
6.5. Scaling ML Services with Kubernetes/24.Deploying containers in a Kubernetes cluster.mp45.22MB
6.5. Scaling ML Services with Kubernetes/25.Scaling up a Kubernetes cluster.mp45.01MB
6.5. Scaling ML Services with Kubernetes/26.Autoscaling a Kubernetes cluster.mp41.6MB
7.6. ML Services in Production/27.Monitoring service performance.mp43.97MB
7.6. ML Services in Production/28.Service performance data.mp44.6MB
7.6. ML Services in Production/29.Docker container monitoring.mp42.72MB
7.6. ML Services in Production/30.Kubernetes monitoring.mp42.83MB
8.Conclusion/31.Best practices for scaling ML.mp43.63MB
8.Conclusion/32.Next steps.mp42.17MB

友情提示

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