The advancements in AI and Machine Learning globally have significantly transformed various industries. The use of latent factors in the AI domain has proven useful in revealing hidden patterns to enhance machine learning models across industries.
We talked to expert Rohan Singh Rajput, a distinguished leader in the field of personalization– with over eight years of experience in analytics, data science, machine learning, and AI, who shed light on the latent factors of artificial intelligence that revolutionize machine learning across industries.
Latent factors refer to the hidden variables inferred from mathematical models, which help decipher the underlying patterns in the data. Concerning Rohan’s work at Ticketmaster, latent factors were crucial in delivering machine learning interpretability and model monitoring systems using a matrix factorization-based model. Latent factors enabled the extraction of hidden features, resulting in an enhanced recommendation system and more relevant and personalized event suggestions for the users.
Rohan’s journey at Headspace, a global leader in mindfulness and mental health resources, is marked by the innovative implementation of machine learning models to enhance user engagement and foster long-term habit formation. His leadership in designing and deploying a machine learning-based model for a personalized recommendation system has not only improved engagement but also significantly supported users in their mental health journey. The article “Mindful Experimentation” on Headspace’s engineering blog provides a deep dive into how A/B testing, a framework established by Rohan, has been instrumental in evaluating recommendation system performance. The meticulous approach towards experimentation and data-driven decision-making has enabled the team to ensure that the deployed models align with user needs and business objectives.
Furthermore, his expertise is not confined to a single industry. Rohan’s work in predictive modeling at Owned Outcomes Inc., where he collaborated with pharmacies and hospitals to predict patient risk scores using EHR and census data, demonstrates the versatility and impact of machine learning across different sectors. The transition from a rule-based model to an ensemble machine learning predictive model, using regression and XGBoost, improved patient adherence by 21%, thereby reducing costs for healthcare insurance providers.
He also shed light on the latent factors that have been a catalyst in enhancing the predictive accuracy of models by uncovering the hidden patterns and intrinsic structures in the data. Rohan’s work, especially in developing a Natural Language Processing Model for artists and events at Ticketmaster, underscores the profound impact of latent factors in improving recommendation systems. By extracting features using Spacy, Fasttext, and sk-learn, and deploying the application on AWS infrastructure, Rohan has demonstrated how latent factors can be effectively utilized to enhance user experience and engagement.
Rohan Singh Rajput’s journey, from developing predictive models in healthcare to enhancing user personalization and engagement in mindfulness apps, exemplifies the transformative potential of AI-powered technologies. His work, particularly in utilizing latent factors to enhance machine learning models, has not only revolutionized recommendation systems but also significantly impacted decision support mechanisms across industries. As AI continues to evolve, the insights and innovations brought forth by experts like Rohan will undoubtedly pave the way for more breakthroughs, shaping the future of machine learning and data science.
