Amazon Sagemaker got you started. Let Labelbox take you to the next level.
Why choose Labelbox over Amazon Sagemaker?
Everything in one place
Leverage an end-to-end system that offers everything you need to improve model performance. From data curation and labeling operations to model training and diagnostic workflows, we help you build quality AI products, faster than ever.
Out-of-the-box setup & intuitive UI
Labelbox's intuitive interface is easy for any team to use, with self-serve onboarding and guided implementation across the platform to help you get set up quickly.
Maximum configurability & flexibility
We understand that every ML project might look a little different. Easily create customizable review workflows, flexible ontologies, and unique labeling tasks based on your project-specific needs.
Reducing our data requirements is huge because we can get the same amount of improvement in our model’s performance in half the time and with half the effort. This was enabled through targeting the model’s weaknesses with Labelbox’s Model product and then being able to prioritize the right data through Catalog. By doing so, we’ve reduced our labeling spend and data needs by over 50%.
Noe Barrell, ML Engineer
But I already use Amazon Web Services and have all my data in an S3 bucket...
While it may seem convenient to use Sagemaker if you already use S3, it might actually take you longer to get started. Rather than being an out-of-the-box platform, Sagemaker requires extensive investment, time, and expertise to customize and build.
As an alternative, we built Labelbox to save you time and resources. Our out-of-the-box platform and self-serve UI allows teams to intuitively navigate our workflows and get started right away. Pre-built configuration and workflows make it easy for teams to focus on model production.
We also offer IAM delegated access integrations for all cloud users, including configurability with Amazon S3, Google Cloud Storage, and Microsoft Blob Azure Storage. Rather than being limited to just the S3 ecosystem, you can securely and seamlessly host data in your preferred cloud storage provider and use IAM roles and policies to control access.
But I heard that Amazon Sagemaker meets the needs of my entire team (data scientists, analysts, and MLOps engineers)
While you can try to customize Sagemaker to work for the different roles in your AI team, pay attention to reviews that emphasize how the platform is not necessarily the most intuitive to set up, manage, or navigate (especially for those who are not hardcore programmers).
Labelbox offers features that prioritize team collaboration with easy role-based access control and user management settings. Built-in features for visibility in team performance are also included like detailed feedback loops, adding multiple workforces to a project for additional capacity, and sharing labeling instructions directly in the editor.
But I'm using Sagemaker Ground Truth and don't have a dedicated team in-house
While Sagemaker Ground Truth offers both crowdsourced labeling through Mechanical Turk and Ground Truth Plus, their pricing is based on ‘price per reviewed object’. This means that your labeling spend is likely to grow proportionally to the volume of data labeled. In short? More data = more money spent.
As an alternative, Labelbox Boost bills per screen activity time. We intentionally curate labeling teams and match your project with labelers who are already well-versed in your use case. Time-based billing means you'll only pay for the time spent labeling, allowing for a more accurate assessment of project budgeting and time to completion.
AI has been crucial for us to accomplish our goals and we’re using Labelbox in many of our projects and processes. It allows us to standardize how we create and manage data all in a single location and using their automation features, we’ve seen a reduction in labeling times by 2x.
Amazon Sagemaker works for me now, why should I switch?
Amazon Sagemaker may meet your data annotation needs, however as you grow, you’ll quickly realize you need other tools to help you scale your AI projects. While Sagemaker has a host of applications that may cater to model training, they remain separate workflows and require cumbersome and time-consuming setups.
Labelbox is a platform designed to grow with you. We intentionally designed our Catalog, Annotate, and Model products to cater to active learning workflows to improve model performance. With Labelbox, everything you need to accelerate your model into production is in a single place. In addition, if you already have a model training workflow within Sagemaker, Labelbox fully integrates with the platform to adjust to your processes.
I don’t want an Amazon Sagemaker alternative, I’m looking to improve model performance with a better solution
Labelbox isn't just an Amazon Sagemaker alternative, our all-in-one platform allows you to visualize, connect, and manage data like never before.
Our approach enables AI teams to use workflows, model-assisted labeling, active learning, and advanced data selection methods to improve model performance while keeping data labeling costs to a minimum.
With Labelbox, we’re able to generate high-quality annotations by allowing our team of domain experts and labelers to collaborate more efficiently. The workflow we’ve built queues up all the work for our labelers to create image annotations, which are then sampled and reviewed by experts, and fed into ML models to make better AI diagnoses.