Deploying Scalable Machine Learning for Data Science | 0421A839A023A8286FB1120649680D743A1073DF

Deploying Scalable Machine Learning for Data Science

Name	Deploying Scalable Machine Learning for Data Science	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	186.6 MB
Total Files	65
Hash	0421A839A023A8286FB1120649680D743A1073DF

/5.4. Running ML Services in Containers/
19.Example Docker build process.mp4	11.7 MB
16.Containers bundle ML model components.en.srt	5.5 KB
16.Containers bundle ML model components.mp4	7.0 MB
17.Introduction to Docker.en.srt	6.1 KB
17.Introduction to Docker.mp4	6.2 MB
18.Building Docker images with Dockerfiles.en.srt	8.5 KB
18.Building Docker images with Dockerfiles.mp4	8.1 MB
19.Example Docker build process.en.srt	6.3 KB
20.Using Docker registries to manage images.en.srt	7.3 KB
20.Using Docker registries to manage images.mp4	6.8 MB
/1.Introduction/
01.Scaling ML models.mp4	3.4 MB
02.What you should know.en.srt	1.9 KB
02.What you should know.mp4	2.6 MB
01.Scaling ML models.en.srt	1.6 KB
/2.1. The Need to Scale ML Models/
03.Building and running ML models for data scientists.en.srt	8.7 KB
03.Building and running ML models for data scientists.mp4	10.0 MB
04.Building and deploying ML models for production use.en.srt	8.8 KB
04.Building and deploying ML models for production use.mp4	8.0 MB
05.Definition of scaling ML for production.en.srt	8.1 KB
05.Definition of scaling ML for production.mp4	7.5 MB
06.Overview of tools and techniques for scalable ML.en.srt	8.1 KB
06.Overview of tools and techniques for scalable ML.mp4	10.3 MB
/3.2. Design Patterns for Scalable ML Applications/
07.Horizontal vs. vertical scaling.en.srt	6.7 KB
07.Horizontal vs. vertical scaling.mp4	6.8 MB
08.Running models as services.en.srt	3.9 KB
08.Running models as services.mp4	3.1 MB
09.APIs for ML model services.en.srt	7.8 KB
09.APIs for ML model services.mp4	8.7 MB
10.Load balancing and clusters of servers.en.srt	7.4 KB
10.Load balancing and clusters of servers.mp4	6.9 MB
11.Scaling horizontally with containers.en.srt	5.0 KB
11.Scaling horizontally with containers.mp4	4.9 MB
/4.3. Deploying ML Models as Services/
12.Services encapsulate ML models.en.srt	4.5 KB
12.Services encapsulate ML models.mp4	4.7 MB
13.Using Plumber to create APIs for R programs.en.srt	6.6 KB
13.Using Plumber to create APIs for R programs.mp4	6.7 MB
14.Using Flask to create APIs for Python programs.en.srt	8.5 KB
14.Using Flask to create APIs for Python programs.mp4	8.9 MB
15.Best practices for API design for ML services.en.srt	2.2 KB
15.Best practices for API design for ML services.mp4	2.1 MB
/6.5. Scaling ML Services with Kubernetes/
21.Running services in clusters.en.srt	5.4 KB
21.Running services in clusters.mp4	4.8 MB
22.Introduction to Kubernetes.en.srt	6.0 KB
22.Introduction to Kubernetes.mp4	6.0 MB
23.Creating a Kubernetes cluster.en.srt	6.3 KB
23.Creating a Kubernetes cluster.mp4	7.9 MB
24.Deploying containers in a Kubernetes cluster.en.srt	5.8 KB
24.Deploying containers in a Kubernetes cluster.mp4	5.5 MB
25.Scaling up a Kubernetes cluster.en.srt	5.3 KB
25.Scaling up a Kubernetes cluster.mp4	5.2 MB
26.Autoscaling a Kubernetes cluster.en.srt	1.9 KB
26.Autoscaling a Kubernetes cluster.mp4	1.7 MB
/7.6. ML Services in Production/
27.Monitoring service performance.en.srt	4.2 KB
27.Monitoring service performance.mp4	4.2 MB
28.Service performance data.en.srt	3.9 KB
28.Service performance data.mp4	4.8 MB
29.Docker container monitoring.en.srt	2.8 KB
29.Docker container monitoring.mp4	2.9 MB
30.Kubernetes monitoring.en.srt	2.8 KB
30.Kubernetes monitoring.mp4	3.0 MB
/8.Conclusion/
31.Best practices for scaling ML.en.srt	3.5 KB
31.Best practices for scaling ML.mp4	3.8 MB
32.Next steps.en.srt	2.4 KB
32.Next steps.mp4	2.3 MB
/Exercise Files/
Ex_Files_Scalable_ML_Data.zip	2.2 KB
Total files 65