Manjunath Janardhan
I am a Principal AI Engineer with over two decades of experience transforming complex business challenges through innovative AI solutions. My career is defined by delivering measurable impact, including a patented Intelligent Service Platform that achieved an 80% reduction in operational costs.
Currently at MSG Global Solutions, I lead AI development initiatives for SAP Enterprise applications, with a primary focus on SAP Profitability and Performance Management (PaPM). My work involves architecting and implementing enterprise-scale Generative AI solutions for the PaPM Universal Model, where I integrate vector databases with SAP HANA to significantly enhance information retrieval capabilities.
My previous role at GE Healthcare demonstrated my ability to scale AI solutions globally, where I built on-premises Generative AI systems that boosted developer productivity by 40% across international teams. I specialize in combining open-source Large Language Models with Hybrid-RAG and Agentic techniques, leveraging cloud-native architectures across AWS, Azure, and GCP platforms. My portfolio includes high-impact tools such as MICT GPT, CODE GPT, and Service GPT, with Aspire CODE GPT notably reducing development time for the Aspire CT Product by 30%.
My technical foundation encompasses the complete software development lifecycle, from modernizing monolithic systems to microservices using Java and C++, to containerizing applications with Docker and Kubernetes. I maintain active contributions to open-source NLP projects, reflecting my commitment to advancing the broader AI community.
Professional development remains central to my practice. I regularly engage with the AI community through conferences, workshops, webinars, and hackathons, recently developing a working prototype for a Socratic DSA Tutor. As an industry speaker, Medium blogger, and content creator, I share practical insights on AI implementation strategies and emerging technologies, focusing on mentoring the next generation of AI engineers while driving innovation in enterprise AI applications.
Session
This Live demonstration shows how PyCaret, an open-source low-code machine learning library, can dramatically simplify model training and comparison workflows. PyCaret is democratizing machine learning by empowering anyone to train multiple algorithms and compare their performance with minimal code. Attendees will witness live demonstrations of training various ML algorithms and using automated comparison techniques to select the best performer based on key metrics. Perfect for data scientists, developers, and ML enthusiasts looking to spend less time coding and more time on model analysis and selection.