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AI-Driven Recommendation Engine for a Media Streaming Platform
Industry
Media & Entertainment
Work
Migration, Cloud Architecture Design, CI/CD Implementation
Purpose of the product
Over 18 months, Streamify partnered with our team to design and deploy an AI-driven recommendation engine aimed at enhancing viewer engagement and reducing churn. Leveraging collaborative filtering and content-based algorithms, we built a system capable of processing millions of user interactions daily, delivering hyper-personalized content suggestions.
By migrating from legacy infrastructure to AWS cloud-native services and Kubernetes orchestration, Streamify achieved a 35% increase in average session time while maintaining zero downtime during scaling .
01 / Key product information
Project Description
Streamify, a growing media streaming platform, sought to compete with industry giants by improving content discovery for its 5 million subscribers. Our team of data scientists and engineers modernized their recommendation system using advanced AI/ML techniques.
We implemented a hybrid approach combining collaborative filtering (to analyze user behavior patterns) and content-based filtering (to categorize metadata like genre, director, and actor preferences). This enabled real-time personalization, ensuring users received tailored recommendations based on both historical data and contextual signals (e.g., time of day, device type) .
The solution integrated Apache Spark for large-scale data processing and Kafka for real-time event streaming. By containerizing services with Kubernetes and automating CI/CD pipelines via GitLab, we reduced deployment times by 40% and improved system reliability.
Key product information
Project results
The team
Data Scientists: 3 (Python, Pandas, SciKit-Learn)
Backend Engineers: 2 (Node.js, Apache Kafka)
DevOps Engineers: 2 (Kubernetes, AWS)
QA Engineers: 1 (Load testing, A/B testing)
Project Manager: 1 (Agile coordination)
Key Challenges
Balancing latency for real-time recommendations with computational costs.
Integrating legacy user data (structured and unstructured) into modern ML pipelines.
Ensuring GDPR compliance while anonymizing user behavior data.
Client Collaboration
Bi-weekly sprint reviews with Streamify’s CTO and product team.
Co-developed a feedback loop for A/B testing recommendation variants.
Shared Confluence documentation for transparency in model training and data governance.
Tools & technologies
Spanning 30+ verticals and 25+ technologies, our team has designed and implemented innovative solutions to suit even the most unique needs.
01 AI/ML
Python (Pandas, NumPy, SciKit-Learn)
Apache Spark
02 Backend
Node.js
Kafka
03 Cloud
AWS (EC2, S3, Lambda)
Kubernetes
04 Analytics
Datadog
Snowflake
05 Other Services
GitLab
Jira
Slack
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