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Deploying Scalable Machine Learning for Data Science
magnet:?xt=urn:btih:0421a839a023a8286fb1120649680d743a1073df&dn=Deploying Scalable Machine Learning for Data Science
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文件列表详情
0421a839a023a8286fb1120649680d743a1073df
infohash:
32
文件数量
177.79 MB
文件大小
2018-11-23 04:06
创建日期
2024-11-20 06:41
最后访问
相关分词
Deploying
Scalable
Machine
Learning
for
Data
Science
5.4. Running ML Services in Containers/19.Example Docker build process.mp4 11.13 MB
1.Introduction/01.Scaling ML models.mp4 3.21 MB
1.Introduction/02.What you should know.mp4 2.52 MB
2.1. The Need to Scale ML Models/03.Building and running ML models for data scientists.mp4 9.57 MB
2.1. The Need to Scale ML Models/04.Building and deploying ML models for production use.mp4 7.67 MB
2.1. The Need to Scale ML Models/05.Definition of scaling ML for production.mp4 7.15 MB
2.1. The Need to Scale ML Models/06.Overview of tools and techniques for scalable ML.mp4 9.82 MB
3.2. Design Patterns for Scalable ML Applications/07.Horizontal vs. vertical scaling.mp4 6.53 MB
3.2. Design Patterns for Scalable ML Applications/08.Running models as services.mp4 2.99 MB
3.2. Design Patterns for Scalable ML Applications/09.APIs for ML model services.mp4 8.33 MB
3.2. Design Patterns for Scalable ML Applications/10.Load balancing and clusters of servers.mp4 6.57 MB
3.2. Design Patterns for Scalable ML Applications/11.Scaling horizontally with containers.mp4 4.66 MB
4.3. Deploying ML Models as Services/12.Services encapsulate ML models.mp4 4.45 MB
4.3. Deploying ML Models as Services/13.Using Plumber to create APIs for R programs.mp4 6.43 MB
4.3. Deploying ML Models as Services/14.Using Flask to create APIs for Python programs.mp4 8.51 MB
4.3. Deploying ML Models as Services/15.Best practices for API design for ML services.mp4 1.99 MB
5.4. Running ML Services in Containers/16.Containers bundle ML model components.mp4 6.67 MB
5.4. Running ML Services in Containers/17.Introduction to Docker.mp4 5.93 MB
5.4. Running ML Services in Containers/18.Building Docker images with Dockerfiles.mp4 7.68 MB
5.4. Running ML Services in Containers/20.Using Docker registries to manage images.mp4 6.47 MB
6.5. Scaling ML Services with Kubernetes/21.Running services in clusters.mp4 4.55 MB
6.5. Scaling ML Services with Kubernetes/22.Introduction to Kubernetes.mp4 5.69 MB
6.5. Scaling ML Services with Kubernetes/23.Creating a Kubernetes cluster.mp4 7.52 MB
6.5. Scaling ML Services with Kubernetes/24.Deploying containers in a Kubernetes cluster.mp4 5.22 MB
6.5. Scaling ML Services with Kubernetes/25.Scaling up a Kubernetes cluster.mp4 5.01 MB
6.5. Scaling ML Services with Kubernetes/26.Autoscaling a Kubernetes cluster.mp4 1.6 MB
7.6. ML Services in Production/27.Monitoring service performance.mp4 3.97 MB
7.6. ML Services in Production/28.Service performance data.mp4 4.6 MB
7.6. ML Services in Production/29.Docker container monitoring.mp4 2.72 MB
7.6. ML Services in Production/30.Kubernetes monitoring.mp4 2.83 MB
8.Conclusion/31.Best practices for scaling ML.mp4 3.63 MB
8.Conclusion/32.Next steps.mp4 2.17 MB
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