Kubernetes vs. Amazon ECS for Data Scientists
In this article, we’ll look at two container management solutions — Kubernetes and Amazon Elastic Container Service (ECS) — from a perspective that makes sense for aspiring and current data scientists.
When you’re on your way to having a data science career, you’ll undoubtedly encounter opportunities to use container management solutions. Here, we’ll look at two solutions — Kubernetes and Amazon Elastic Container Service (ECS) — from a perspective that makes sense for aspiring and current data scientists.
If you’re interested in building and using machine learning models as part of your work as a data scientist, tutorials exist that walk you through doing that on both platforms. Amazon provides a step-by-step walkthrough aimed at data scientists.
Then, there is a machine learning toolkit for Kubernetes users called Kubeflow. It’s an open-source, portable and scalable solution. People can apply it to any new or existing Kubernetes deployments.
As you look at comparisons between Kubernetes and ECS, don’t be surprised to find details about availability zones and overall reliability. Choosing a dependable service can help you avoid outages that could temporarily derail your data science projects.
ECS runs in 69 availability zones and 22 regions. Also, ECS falls under the Amazon Web Services (AWS) umbrella. That means it guarantees an uptime rate of at least 99.99%.
Kubernetes emphasizes reliability with an approach that spreads Kubernetes pods among nodes to make them more tolerant of application failures. Moreover, the high availability of Kubernetes extends to both the infrastructure and application level.
Moreover, with the release of Kubernetes 1.2 came support for running single clusters in multiple availability zones offered by cloud providers. However, the selected zones must be in the same region and provided by the same cloud service.
That Kubernetes version offered multiple zone selection automatically to AWS and Google Compute Engine (GCE) users. However, applying the appropriate labels to nodes and volumes creates support for additional cloud services.
Amazon ECS is part of the AWS ecosystem. That brings advantages in some cases and downsides in others. For example, it’s easier and faster to perform the initial setup compared to Kubernetes because you can do it through the AWS Management Console and don’t need to set up the control panel that Kubernetes requires.
However, ECS also has a high level of vendor lock-in, which prevents you from migrating a containerized application to any other provider or platform. In contrast, Kubernetes is an open-source solution that enables moving containers elsewhere, including to hybrid and multi-cloud providers.
AWS regularly releases new products of interest to data scientists, though. For example, in 2018, the company rolled out a machine learning marketplace containing more than 150 new models and algorithms to try. You can also find detailed breakdowns of AWS products that could support your data science projects.
If you already use many products offered by AWS or plan to do so soon, choosing ECS could make more sense than going with Kubernetes. Otherwise, you may find ECS too restrictive.
It’s easy for data scientists to find general information about using ECS, but specifics about how to use ECS in their work are not as common. This reality may mean that people spend more time than expected learning how to apply ECS to data science projects, particularly in the early stages of their usage.
Although some courses for data scientists cover how to use ECS, the content often encompasses only single modules with classes that address a broader assortment of data science topics within the curriculum.
On the other hand, you can quickly find Kubernetes explainers geared toward data scientists. These make it easier to envision why you may want to use it over other services. Similarly, content creators give real-life examples of applying Kubernetes to data science projects, such as predicting customer churn rates.
Some data scientists may want to work with ECS or Kubernetes while paying for the costs themselves instead of relying on an employer. In such cases, calculating the expenses associated with ECS can pose fewer challenges because AWS offers price calculators to avoid surprises.
There are also free tiers associated with AWS products. That sounds like a good thing at first. However, users report receiving large bills after their eligibility for free services expired without them realizing it.
The primary reason why calculating the pricing with Kubernetes is not as simple is the growing network of certified service providers that can help people start using Kubernetes. Some of those entities offer products with built-in Kubernetes support, too.
That means the prices you pay for using Kubernetes through those providers will vary. The ideal approach is to create a list of products or companies that interest you and offer Kubernetes support. Then, take the time to research their pricing structures and see which ones seem most appropriate for your budget and the extent of data science work you want to do with Kubernetes.
This overview emphasizes why data scientists should not make rushed decisions when choosing between Kubernetes and ECS. Both have pros and cons that could ultimately affect data science projects.
However, allowing enough time to learn about each solution is a smart move. Consider any projects in your pipeline that may apply to your use of a containerization solution. Then, review each product's features and determine which ones are most relevant to your situation and expectations. Scrutinizing each option this way will lead to well-informed decisions.
Bio: Devin Partida is a big data and technology writer, as well as the Editor-in-Chief of ReHack.com.
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