Kartik Sehgal
Creating a machine learning (ML) system from scratch is a challenging task that requires a deep understanding of various phases. It all starts with defining the problem at hand, aligning it with the business objectives. Gathering relevant and high-quality data is another hurdle, often involving data from different sources. Kartik Sehgal, an experienced ML engineer, emphasizes the importance of understanding the business goals and the data behind the model to ensure its success.
Processing and cleaning the raw data is crucial for analysis, requiring domain knowledge to select relevant features. Even deploying the model comes with its own set of challenges like scalability and integration. Kartik shares his experience leading a groundbreaking ML project at a major online retailer and cloud service provider, showcasing his expertise in developing scalable ML systems.
His role in designing and implementing a real-time ML inference system for optimizing product pricing demonstrates his ability to handle complex projects. Kartik’s passion for software engineering and ML shines through as he navigates the intricacies of training and inference to improve revenue and profit margins for sellers.
Despite facing challenges, Kartik and his team successfully delivered a minimum viable product within a short timeframe, marking a significant milestone for the organization. This project not only showcased Kartik’s transition to a full-fledged ML engineer but also fueled his passion for innovation and modeling.
After the project’s success, Kartik continued to work on search advertising systems, expanding his expertise and taking on more modeling responsibilities. His journey in the search domain reflects his commitment to growth and innovation, driven by the potential of large language models and advancements in natural language processing.
2024-07-08 09:15:02
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