How Intertec Built a Centralized MLOps Platform to Scale AI in E-Commerce
Value Delivered
40%
Reduction in deployment time due to CI/CD integration, resulting in faster time-to-market.
INDUSTRY
E-commerce
PROJECT DURATION
10 years
LOCATION
Worldwide
CLUTCH REVIEW
5
Client Bio
An e-commerce platform needed to serve customers around the globe with accurate, localized content. But each department had its own system for translating product descriptions, support articles, and marketing materials. This patchwork approach led to inconsistencies and made it hard to maintain a single, unified brand message.
Situation
One of the world's largest shopping engagement platforms faced challenges in managing its growing Machine Learning (ML) initiatives. With multiple ML projects running simultaneously, data scientists relied on locally installed MLOps tools for experiment tracking, model registration, and asset versioning. This decentralized approach made collaboration difficult, as experiment results and assets (models and datasets) couldn’t be easily shared across teams.
To address these challenges, the client partnered with Intertec to build a centralized MLOps platform. The solution integrates backend and artifact stores, ensuring seamless data tracking, asset sharing, and efficient workflow management. By leveraging AWS cloud services, the new platform enables scalability, automation, and secure collaboration, improving overall ML productivity.
Solution
Solution
Intertec built a centralized, cloud-based MLOps platform that allows the leading ecommerce platform to track ML experiments, manage model assets, and streamline collaboration. The solution integrates backend storage for experiment data and an artifact store for ML models and datasets.
- Unified MLOps platform - All ML teams now use a single platform for experiment tracking, model registration, and asset versioning, eliminating inefficiencies.
- Scalable cloud infrastructure - Hosted on AWS, the platform scales dynamically based on demand using services like EC2, AutoScaling, and Load Balancing.
- Automated deployment and monitoring - By integrating AWS CodePipeline and CloudWatch, the client’s teams can continuously deploy updates and monitor system performance in real time.
- Improved collaboration - Data scientists can now easily share experiment results, models, and datasets, fostering teamwork and accelerating innovation.
Impact
- Siloed ML workflows - Each data scientist worked independently with locally hosted MLOps tools, making it difficult to share results and insights across teams.
- Lack of versioning and governance - Without a centralized model registry, teams struggled to track different versions of ML models and datasets.
- Limited collaboration - Experiment results, model performance data, and assets couldn’t be accessed in real time by other teams, leading to duplicated efforts and slow decision-making.
- Scalability concerns - Locally set up MLOps tools weren’t scalable, making it challenging to support growing data and computation needs. Every request had to pass through several engineers and tools before it was ready, creating delays and potential errors