Mindvalley has also significantly improved the response times and reliability of its product features. By centralizing its infrastructure, the company has also simplified its operational complexity, reducing manual maintenance for the engineers behind its products and giving them more time to focus on creative, innovative projects. 

With the analytics and added visibility into business metrics in Cloud Monitoring, the company’s leadership team can make more informed, data-driven decisions. These decisions help them more clearly pursue the company’s strategic vision and empower its users.   

"Overall, Google Cloud has streamlined our processes, boosted efficiency, and supported our continuous growth," says Singh. "This has proven invaluable as we seek to open up new avenues of continuous learning for life transformation around the world."

Moving its machine learning (ML) pipelines from Kubernetes clusters to Vertex AI was instrumental to Mindvalley's successful migration. As it began adopting AI, the startup found it increasingly difficult to manage its growing ML infrastructure. 

With the switch to Vertex AI, the company has become more efficient and can better support creative tasks. On the technical side, for example, Mindvalley has all but eliminated downtime on ML API endpoints and maintains a service availability of nearly 99%. Additionally, it has a 99.9% success rate in deploying ML pipelines—including both model training and prediction. By revamping the entire MLOps process, Mindvalley has reduced ML infrastructure costs by approximately 20%, savings it is able to reinvest in more AI-driven technologies.

"By migrating our ML pipelines to Vertex AI we have achieved significant improvements that have combined to increase productivity and operational efficiency, leading to success in numerous projects that enrich the user experience for members worldwide, while boosting our business model," says Singh. 

Mindvalley has taken the time and money previously spent on maintenance and used those resources to implement six AI recommendation engine APIs. These APIs deploy collaborative filtering and content-based filtering with TensorFlow, Cloud Run, and Vertex AI, and together have increased click-through-rates by 30% year-over-year. 

The company also built a search-and-chat assistant powered by a Large Language Model (LLM) through the integration of  Gemini Pro and open-source framework LangChain. Meanwhile, the team built an AI-driven campaign engine which provides more relevant and engaging email campaigns that leverage demographic information and behavioral data. Lastly, Mindvalley implemented an ML model for churn prediction and customer segmentation that aids in targeted customer retention efforts. The model identified potential leavers, resulting in an eight percent reduction in overall churn.