Resources / SageMaker vs. Valohai

An Alternative for SageMaker

SageMaker vs. Valohai

SageMaker is a machine learning platform for AWS. For AWS-native teams, SageMaker might be their first contact to MLOps and machine learning platforms, but are there reasons to look outside the AWS ecosystem?

In this comparison paper, we compare Valohai and SageMaker.

SageMaker vs Valohai comparison paper

What is SageMaker?

SageMaker is a machine learning platform for AWS. The platform consists of multiple services under the SageMaker umbrella that allow data scientists to prepare data, build and train models and deploy them on AWS. For AWS-native teams, SageMaker might be their first contact to MLOps and machine learning platforms, but are there reasons to look outside the AWS ecosystem?

There are many alternatives for SageMaker and each come with their own approaches to empowering model development and productization. The Valohai MLOps platform is one such alternative.

Elephant in the room
The elephant in the room is that SageMaker is only available for AWS, and teams who need to utilize other clouds need to look for alternatives elsewhere.

First of all, do I even need a platform for MLOps?

Are you a one-person show working on early-stage experiments? Probably not.
Are you part of a data science team that has models in production? Yes, you do.

MLOps platform, like Valohai, SageMaker, or Kubeflow, help systematize and automate machine learning work in a way that allows teams to scale.

  • Automatic experiment tracking keeps work organized and shared.
  • Managed infrastructure enables the usage of cloud computing quickly and easily.
  • Model deployment allows data science teams to get to production independently.
  • Machine learning pipelines focus teams on building ML systems that fix themselves.

So what makes Valohai different from SageMaker 👇

SageMaker is not available for GCP, Azure, or OpenStack; Valohai is.

For many modern R&D teams, cloud lock-in is not an option. Locking development to a single cloud vendor can be cost-prohibitive, and the further along teams get with machine learning and deep learning, the bigger the cost factor becomes. Limiting yourself from the Azure or Google Cloud ecosystems can also exclude you from certain hardware advancements (such as TPU-support, which is only available on GCP).

With Valohai, you can optimize for cost and take advantage of the race to the bottom.

  • SageMaker is only available for AWS. On-premise solutions are limited to AWS-managed on-premise hardware.
  • Valohai is available for AWS, GCP, Azure, OpenStack, and any on-premise setup. Choose between any type of multi-cloud or hybrid cloud setup.

SageMaker is not technology agnostic; Valohai is.

Much of the SageMaker experience lives within the SageMaker Studio IDE. This approach may be great for individuals who start with machine learning without prior workflows but may not be ideal if you’ve built your perfect setup in a local environment. The integrated approach also limits what languages and frameworks are supported.

With Valohai, you are not forced to make technology choices. We support any language and any framework.

  • SageMaker is built integrated IDE-first, which means that the full experience is limited to the hosted IDE and supported languages, i.e., Python.
  • Valohai is a completely technology and tool agnostic. The development environment is your own, and any code can be run on Valohai through the command line, API, or Web UI.

If you are looking for a GCP, Azure, or on-premise alternative for SageMaker, Valohai is it.

Now let’s be real here; we are biased. We believe that we’ve built the best experience for developing and maintaining machine learning models. But what’s not up to one’s beliefs is that if you need to run any code on any hardware, you need a SageMaker alternative.

Learn more about the key differences in the comparison whitepaper above.