When data pipelines - preparing, labeling, managing and curating training data - are disconnected from the rest of the model workflow, ML teams often struggle with slower iteration cycles and higher inefficiencies. Up to 90% of failures in ML development are due to challenges integrating the model with quality production data. This idea guided our integration with AWS - labeled data from SuperAnnotate enters SageMaker in the same environment that supports training and evaluation.
We are proud to share that SuperAnnotate was nominated for and collaborated with SageMaker Partner Incubator to build direct integrations with Amazon SageMaker’s product teams. This integration gives ML teams a clear and connected path from labeling to training inside the AWS environment.
“The SageMaker Incubator gave us exactly what we needed to move fast – deep technical support and tight collaboration, helping us deliver a truly integrated solution at scale. It’s been an incredible experience building alongside AWS to deliver a unified workflow that brings greater value and speed to our mutual customers.” – Vahan Petrosyan, Cofounder and CEO
Why Integration Matters When Choosing An Annotation Platform
Teams building advanced AI systems use labeled data in every stage of model development – from training to supervised fine-tuning and evaluation. The annotation platform that handles this work needs to fit into the environment where your models live. When labeling and training happen in disconnected systems, teams carry a large amount of extra coordination.
An annotation platform that integrates to your existing data pipelines removes this hurdle. Labeled data is exported directly to where training and evaluation already take place. This creates a seamless data-to-model pipeline that reduces manual data management, enabling teams to focus more on model development and less on repetitive data handling. SuperAnnotate supports this flow by providing teams with model-ready data that comes from their domain and expert knowledge. This custom data then becomes a training foundation inside Sagemaker and drives higher model performance.
Integration between labeling and training environments allows teams to move through their work in a steady rhythm instead of stopping and restarting at every stage. When data teams and ML engineers work inside one continuous workflow, iteration becomes faster and more effective. Newly labeled data moves into the training environment as soon as it is created, and fine tuning or evaluation can begin the moment the dataset appears. With fewer handover steps between stages, teams gain time and the whole path from annotation to model development stays connected in a clear and continuous way.
How SuperAnnotate and Amazon SageMaker Worked Together
The incubator brought our team together with AWS engineers who work closely with enterprise customers. Through this program, we shaped an approach that places annotated data directly into the same storage structure that powers SageMaker. This came from many conversations about how teams build and ship models, how they track versions, and how they prepare data for training cycles.
The Technical Solution Explained
All annotated datasets created inside SuperAnnotate are stored in SageMaker lakehouse managed S3 buckets. The data arrives with consistent metadata and formats, which allows teams to open it directly through Amazon S3 URIs inside the SageMaker Unified Studio environment. These S3 URIs serve as the single reference point for every stage of model work, so the data stays unified across environments and teams avoid extra handling.
This architecture simplifies 2 main common workflows.
- Training pipelines can take a labeled dataset and begin fine tuning with it.
- Evaluation cycles can use newly labeled production data, run inference, and compare outputs without further preparation.
The SageMaker lakehouse managed S3 layer serves as a central data point for these stages. It maintains version control, keeps the information consistent and helps teams move smoothly from annotation to model development within AWS.
What This Means for Teams Using Amazon SageMaker
This integration brings a clear improvement in time and focus to teams. When labeled data arrives in the training environment without additional handling, engineers gain:
Speed & quality benefits
The integration supports faster model development because annotated data appears in the location where teams already build and evaluate models. This reduces the gap between each round of improvement.
Seamless data pipelines
The shared S3 foundation places annotation, training, and evaluation into the same environment. Each stage uses the same data structure, which supports predictable and stable workflows.
Purchase SuperAnnotate on AWS Marketplace
SuperAnnotate is available for purchase on the AWS Marketplace. Customers who purchase via this method will see their SuperAnnotate license on their AWS bill. This allows customers to consolidate vendors and spending, potentially unlocking greater incentives with AWS.
Final Thoughts
The SuperAnnotate and SageMaker integration reshapes how data moves through the machine learning lifecycle. It brings annotation, training, and evaluation into one continuous pipeline, built directly on AWS.
For teams already building in SageMaker, this integration offers a cleaner route from human insight to model performance.
To explore how this workflow can fit into your setup, reach out to the SuperAnnotate team.
