MLOps pipeline for DopikAI

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Amazon Simple Storage Service (Amazon S3), AWS Step Functions, Amazon SageMaker Ground Truth, AWS Lambda, Amazon SageMaker Pipelines, SageMaker Training Jobs, SageMaker Processing Jobs, AWS CodeBuild, AWS Elastic Kubernetes Service (EKS)

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DopikAI

Established in 2019, DopikAI is an artificial intelligence company specializing in digital conversion solutions that leverage advanced technologies like image processing, computer vision, and natural language processing (NLP). With a strong focus on innovation, DopikAI has developed cutting-edge solutions in text and image recognition, including legal documents and ID card recognition, catering to industries that require efficient, accurate, and automated data extraction. By integrating AI into everyday processes, DopikAI empowers businesses to enhance operational efficiency and make smarter, data-driven decisions.

DopikAI faced a significant challenge in building an efficient and cost-effective MLOps pipeline to manage the labeling, training, and deployment of models in operation. The complexity of coordinating these tasks, especially with the need for scalable and automated processes, demanded a robust solution that could optimize resources and reduce operational costs. To address these challenges, DopikAI sought a tech partner capable of delivering a comprehensive and innovative solution. SotaTek was the perfect fit, known for being an AWS Well-Architected Partner and holding the AWS Advanced Tier Services status, which underscored their expertise in implementing AWS MLOps best practices.

To address DopikAI's challenges, SotaTek implemented AWS MLOps best practices through three key solutions:

1. Data Storage and Labeling Management

SotaTek began by utilizing Amazon Simple Storage Service (Amazon S3) to store raw image data, which provided a scalable and low-cost solution for managing large datasets. To streamline the complex labeling workflow, AWS Step Functions were employed, allowing for seamless coordination of tasks from data preparation to labeling. By integrating Amazon SageMaker Ground Truth, SotaTek enabled automated labeling with the flexibility of using both machine learning models and managed human resources. AWS Lambda further enhanced the process by handling data preparation, initiating labeling tasks, and storing the labels in the Amazon SageMaker Feature Store, ensuring that all aspects of the labeling pipeline were efficiently managed.

2. Model Training and Deployment Pipeline

For the training and deployment of DopikAI’s models, SotaTek implemented Amazon SageMaker Pipelines, which provided a robust framework to automate the entire machine learning workflow. SageMaker Training jobs were used to automate the model training process, while SageMaker Processing jobs facilitated data preparation and model performance evaluation. This integration of essential SageMaker functionalities allowed SotaTek to construct a streamlined pipeline that reduced manual intervention, minimized errors, and ensured consistent model performance, all while keeping operational costs in check.

3. Automated Model Release and Application Hosting

To ensure that new model versions were seamlessly deployed, SotaTek used AWS CodeBuild in conjunction with AWS Step Functions to build Docker images and enable GitOps for automated releases across development, UAT, and production environments. This approach not only expedited the deployment process but also ensured that updates were consistently and accurately applied. For application hosting, SotaTek leveraged AWS Elastic Kubernetes Service (EKS), which offered a scalable and reliable platform for DopikAI’s web applications. By utilizing GitOps and auto-scaling features within AWS, SotaTek was able to optimize resource usage, enhance application performance, and achieve significant cost savings.

• Automated MLOps pipeline reduced manual efforts and streamlined workflows, boosting operational efficiency.
• Optimized storage and scalable infrastructure significantly lowered operational costs.
• Seamless model deployment across environments enhanced DopikAI’s agility and ability to scale quickly.
• Improved ability to scale AI solutions, adapting swiftly to evolving business needs.

Thanks to the strategic implementation of AWS MLOps best practices, DopikAI effectively overcame their challenges in developing an efficient and cost-effective MLOps pipeline. SotaTek's solutions streamlined and automated DopikAI's workflow, reducing manual efforts and operational costs while enhancing the accuracy and speed of data labeling and model training. This approach facilitated seamless model deployment and scaling, allowing DopikAI to optimize their operations and accelerate time to market. Ultimately, these improvements provided DopikAI with a flexible and scalable infrastructure, helping them maintain a competitive edge in AI-driven digital conversion solutions and adapt swiftly to evolving business needs.

DopikAI was pleased with the outcomes of SotaTek’s solutions, which effectively addressed their key challenges and enhanced their MLOps pipeline. Impressed by the impactful results and the seamless collaboration, DopikAI is eager to establish a long-term partnership with SotaTek. They see SotaTek as a trusted tech partner, with whom they look forward to working closely in the future to drive ongoing innovation and success in their AI-driven initiatives.

If your business faces challenges similar to those of DopikAI, such as building an efficient MLOps pipeline or optimizing data workflows, contact us. Our team at SotaTek is equipped with the expertise and cutting-edge AWS solutions to address your needs effectively and drive your success. Let us help you streamline your operations and achieve your goals with our tailored technology solutions.