种子简介
种子名称：
Deploying Scalable Machine Learning for Data Science
文件类型：
视频
文件数目：
32个文件
文件大小：
177.79 MB
收录时间：
2018-11-23 04:06
已经下载：
3次
资源热度：
118
最近下载：
2024-6-18 18:08
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种子包含的文件
Deploying Scalable Machine Learning for Data Science.torrent
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
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