Amazon Elastic Kubernetes Service (EKS) vs. Amazon SageMaker AI vs. Portainer

It is well suited when you want to have a Kubernetes cluster in AWS Cloud and want to avoid all the management overhead of maintaining your own cluster in terms of the control plane. EKS seems to be lacking in features when compared with AKS and GKE. Backups, service mesh, and monitoring have a lot of room for improvements.

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It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.

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Many developers, especially lesser experienced developers, don't have a really good background in setting up containers from the command line. Portainer is invaluable to them. Giving a UI to them gives them much more confidence and allows them to learn properties and capabilities of containers under far less stress. On the flip side of this, giving then a UI on a production system can lead to chaos...never give junior developers access to production servers.

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• Upgrade the kubernetes clusters to the latest version with a single click
• Auto scaling policies to automatically scale the nodes
• Detailed logs and events on the cluster within the EKS clusters portal, cloudwatch logs and metrics

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• Machine Learning at scale by deploying huge amount of training data
• Accelerated data processing for faster outputs and learnings
• Kubernetes integration for containerized deployments
• Creating API endpoints for use by technical users

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• Separating server maintenance with application development, providing a clear user interface for developers who don't want to worry about the underlying server.
• RBAC for container deployment linked to a SAML IDP, not something particularly easy in a native Docker instance but point and shoot in Portainer, allowing the use of Azure / Okta etc to provide user access.
• Image management with multiple repositories is super clear and reduces incidents

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• AWSIAM integration with Kubernetes RBAC could be better.
• Enabling some add-ons like service mesh, and monitoring will be nice instead of having to install them yourself after the creation of the cluster.
• EKS bootstrap time could be faster ...

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• It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
• Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

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• When setting up static IPs for a new container, having the used ones for a specific network at hand would be cool or something like a mini IPAM of some sort...
• Using the developertools to see "oh, it's a 40x or 500" when something doesn't seem to load because the ui just states "Didn't work" is kinda annoying. expandable toasts or something would be nice.

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It is an excellent tool.

Cluster maintanence is reduced, easier to deploy resources, great observability insights

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Accessibility for Non-Experts: even with some people having a bit longer on-boarding it is still very simple Quick setup is insanely useful, we can get it running in 10 seconds after installing docker Portainer has once again super clean UI and is very user friendly. Deployment/monitoring and management are super easy. I can tell just from a glance if something is out of date (watching at you Watchtower not doing your job for some reason)

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One of their staff members jumped on a video call immediately with me and led me through the problem and solution during a quick session of screen sharing. In this day and age that is above and beyond, especially when it comes to software. It took approximately 5-10 minutes to diagnose and fix, including pleasantries!

It is really easy. Just follow the documentation.

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It feels like AWS is behind the EKS race, the only advantage I'm able to see right now is the support of IPv6, however, trying to promote AWS alternatives that are different from the market and more like a vendor locking solutions like ECS/Fargate have kept AWS behind and focusing on the wrong things. EKS needs to really improve its integration with the Kubernetes ecosystem and have an enterprise solution for monitoring, backups, and service mesh.

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Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.

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Portainer takes the cup in terms of usability and features. It is also more useful for smaller deployments, whereas Kubernetes in our opinion and experience, could probably be more suited to certain other use cases. Portainer is also a fresh feel among all the preexisting container management solutions and brings positivity and a new breeze in the industry.

• Good performance of platform without hiccups
• Less number of people required to manage cluster
• Easier to deploy new microservices

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• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

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• Increased productivity: Portainer's user-friendly interface and streamlined container management can help increase the productivity of IT teams.
• Cost savings: By simplifying container management, Portainer can help reduce the time and resources required to manage container environments, potentially leading to cost savings.

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