Deploying Scalable Machine Learning for Data Science

Creation Time	June 30, 2024, 2:44 a.m.
Last Access Time	Nov. 19, 2024, 5:40 p.m.
File Size	178.0 MB
Keywords	for Science Scalable Machine Learning Data Deploying
Total Requests	16
Total Files	65
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5.4. Running ML Services in Containers/19.Example Docker build process.mp4 11.1 MB
2.1. The Need to Scale ML Models/06.Overview of tools and techniques for scalable ML.mp4 9.8 MB
2.1. The Need to Scale ML Models/03.Building and running ML models for data scientists.mp4 9.6 MB
4.3. Deploying ML Models as Services/14.Using Flask to create APIs for Python programs.mp4 8.5 MB
3.2. Design Patterns for Scalable ML Applications/09.APIs for ML model services.mp4 8.3 MB
5.4. Running ML Services in Containers/18.Building Docker images with Dockerfiles.mp4 7.7 MB
2.1. The Need to Scale ML Models/04.Building and deploying ML models for production use.mp4 7.7 MB
6.5. Scaling ML Services with Kubernetes/23.Creating a Kubernetes cluster.mp4 7.5 MB
2.1. The Need to Scale ML Models/05.Definition of scaling ML for production.mp4 7.1 MB
5.4. Running ML Services in Containers/16.Containers bundle ML model components.mp4 6.7 MB
3.2. Design Patterns for Scalable ML Applications/10.Load balancing and clusters of servers.mp4 6.6 MB
3.2. Design Patterns for Scalable ML Applications/07.Horizontal vs. vertical scaling.mp4 6.5 MB
5.4. Running ML Services in Containers/20.Using Docker registries to manage images.mp4 6.5 MB
4.3. Deploying ML Models as Services/13.Using Plumber to create APIs for R programs.mp4 6.4 MB
5.4. Running ML Services in Containers/17.Introduction to Docker.mp4 5.9 MB
6.5. Scaling ML Services with Kubernetes/22.Introduction to Kubernetes.mp4 5.7 MB
6.5. Scaling ML Services with Kubernetes/24.Deploying containers in a Kubernetes cluster.mp4 5.2 MB
6.5. Scaling ML Services with Kubernetes/25.Scaling up a Kubernetes cluster.mp4 5.0 MB
3.2. Design Patterns for Scalable ML Applications/11.Scaling horizontally with containers.mp4 4.7 MB
7.6. ML Services in Production/28.Service performance data.mp4 4.6 MB
6.5. Scaling ML Services with Kubernetes/21.Running services in clusters.mp4 4.6 MB
4.3. Deploying ML Models as Services/12.Services encapsulate ML models.mp4 4.5 MB
7.6. ML Services in Production/27.Monitoring service performance.mp4 4.0 MB
8.Conclusion/31.Best practices for scaling ML.mp4 3.6 MB
1.Introduction/01.Scaling ML models.mp4 3.2 MB
3.2. Design Patterns for Scalable ML Applications/08.Running models as services.mp4 3.0 MB
7.6. ML Services in Production/30.Kubernetes monitoring.mp4 2.8 MB
7.6. ML Services in Production/29.Docker container monitoring.mp4 2.7 MB
1.Introduction/02.What you should know.mp4 2.5 MB
8.Conclusion/32.Next steps.mp4 2.2 MB
4.3. Deploying ML Models as Services/15.Best practices for API design for ML services.mp4 2.0 MB
6.5. Scaling ML Services with Kubernetes/26.Autoscaling a Kubernetes cluster.mp4 1.6 MB
2.1. The Need to Scale ML Models/04.Building and deploying ML models for production use.en.srt 8.6 KB
2.1. The Need to Scale ML Models/03.Building and running ML models for data scientists.en.srt 8.5 KB
4.3. Deploying ML Models as Services/14.Using Flask to create APIs for Python programs.en.srt 8.3 KB
5.4. Running ML Services in Containers/18.Building Docker images with Dockerfiles.en.srt 8.3 KB
2.1. The Need to Scale ML Models/06.Overview of tools and techniques for scalable ML.en.srt 7.9 KB
2.1. The Need to Scale ML Models/05.Definition of scaling ML for production.en.srt 7.9 KB
3.2. Design Patterns for Scalable ML Applications/09.APIs for ML model services.en.srt 7.6 KB
3.2. Design Patterns for Scalable ML Applications/10.Load balancing and clusters of servers.en.srt 7.2 KB
5.4. Running ML Services in Containers/20.Using Docker registries to manage images.en.srt 7.1 KB
3.2. Design Patterns for Scalable ML Applications/07.Horizontal vs. vertical scaling.en.srt 6.5 KB
4.3. Deploying ML Models as Services/13.Using Plumber to create APIs for R programs.en.srt 6.4 KB
5.4. Running ML Services in Containers/19.Example Docker build process.en.srt 6.2 KB
6.5. Scaling ML Services with Kubernetes/23.Creating a Kubernetes cluster.en.srt 6.1 KB
5.4. Running ML Services in Containers/17.Introduction to Docker.en.srt 6.0 KB
6.5. Scaling ML Services with Kubernetes/22.Introduction to Kubernetes.en.srt 5.8 KB
6.5. Scaling ML Services with Kubernetes/24.Deploying containers in a Kubernetes cluster.en.srt 5.7 KB
5.4. Running ML Services in Containers/16.Containers bundle ML model components.en.srt 5.4 KB
6.5. Scaling ML Services with Kubernetes/21.Running services in clusters.en.srt 5.3 KB
6.5. Scaling ML Services with Kubernetes/25.Scaling up a Kubernetes cluster.en.srt 5.1 KB
3.2. Design Patterns for Scalable ML Applications/11.Scaling horizontally with containers.en.srt 4.8 KB
4.3. Deploying ML Models as Services/12.Services encapsulate ML models.en.srt 4.4 KB
7.6. ML Services in Production/27.Monitoring service performance.en.srt 4.1 KB
3.2. Design Patterns for Scalable ML Applications/08.Running models as services.en.srt 3.8 KB
7.6. ML Services in Production/28.Service performance data.en.srt 3.8 KB
8.Conclusion/31.Best practices for scaling ML.en.srt 3.4 KB
7.6. ML Services in Production/30.Kubernetes monitoring.en.srt 2.8 KB
7.6. ML Services in Production/29.Docker container monitoring.en.srt 2.7 KB
8.Conclusion/32.Next steps.en.srt 2.4 KB
Exercise Files/Ex_Files_Scalable_ML_Data.zip 2.2 KB
4.3. Deploying ML Models as Services/15.Best practices for API design for ML services.en.srt 2.1 KB
1.Introduction/02.What you should know.en.srt 1.9 KB
6.5. Scaling ML Services with Kubernetes/26.Autoscaling a Kubernetes cluster.en.srt 1.9 KB
1.Introduction/01.Scaling ML models.en.srt 1.5 KB

Deploying Scalable Machine Learning for Data Science

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