MLOps in Action: Sunix’s Leap Toward Faster, Smarter AI/ML Solutions with Rapyder

Client:

Sunix AI Private limited

Introduction:

Sunix AI Private Limited, a forward-thinking company in artificial intelligence and machine learning (AI/ML), exemplifies innovation with its transformative journey from manual model training to streamlined one-click deployment powered by MLOps. This evolution underscores Sunix’s dedication to advancing AI/ML for companies across multiple sectors. Through automation and AI, Sunix has positioned itself as a leader, efficiently supporting clients with scalable solutions for data labeling and model deployment.

Business Need:

Sunix faced challenges in its AI/ML workflow, including labour-intensive data labeling, time-consuming hyperparameter tuning, and complex model deployments. High-performance configurations added financial strain, while moving models between staging and production environments required careful coordination. Sunix needed an automated, end-to-end solution to streamline operations, reduce manual effort, and lower costs.

Solution Approach:

To address these challenges, Rapyder introduced a solution with two main components:

1. Data Labeling: Leveraging Amazon SageMaker Ground Truth for efficient data annotation.
2. MLOps Automation: Automating the AI/ML lifecycle using Amazon SageMaker, transforming Sunix’s model training and deployment process into a seamless, one-click operation.

This approach allowed Sunix’s clients to send data for labeling, which was then processed and stored, automatically triggering an MLOps pipeline that managed model training and deployment.

Implementation:

1. Data Labeling:

• Amazon SageMaker Ground Truth enabled efficient data labeling for Sunix’s image and video datasets across categories like image segmentation and object detection.
• Amazon Cognito securely managed credentials for Sunix’s data annotators, ensuring easy access to labeling tasks with client data.

2. MLOps Automation:

• Model Building: Labeled data was stored in Amazon S3, triggering the automated pipeline. Amazon SageMaker then trained the model using built-in algorithms, with AWS CodeBuild and CodePipeline handling the orchestration. The trained models were stored in S3 and registered in SageMaker’s model registry.
• Model Deployment: The model was first deployed to a staging environment using SageMaker, allowing the ML engineer to test and approve it. Once approved, the model was moved to production. AWS SNS provided real-time notifications throughout the process.

3. Monitoring and Alerts:

• AWS CloudWatch monitored the performance of the entire process, with AWS SNS and AWS Lambda sending alerts and handling any issues automatically.

Industry:

IT

Offering:

Data Analytics (MLOps)

AWS Services:

Amazon Sagemaker Ground Truth, Amazon Sagemaker, Amazon S3, AWS CodeBuild, AWS CodePipeline, AWS CodeCommit, Amazon EventBridge, AWS Lambda, Amazon SNS, Amazon Cognito, Amazon Cloudwatch, AWS CloudFormation.

Reaping Rewards:

•  Automation of data labeling and model training reduced Sunix’s deployment time, enabling efficient scaling and management of larger data volumes.
• Faster, consistent model performance enhanced service quality, increasing customer satisfaction and retention.
• Leveraging advanced technology and quick deployment positioned Sunix competitively, attracting more clients.
• Amazon SageMaker Ground Truth and Cognito integration simplified labeling tasks and ensured data security.
• MLOps automation and CloudFormation minimized operational overhead, allowing the team to focus more on business growth.