Dr. Vamsi Mohan Vandrangi
Director of Engineering, QFTL
Bengaluru, India
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Dr. Vamsi Mohan Vandrangi is serving as a Director of Engineering at QFTL. He is awarded with the Next100 CIOs award for the year 2020 and CXO Excellence Award for 2021. He is a Top – 50 global thought leader and influencer for the RPA, Datacenter Technologies, and Cyber Security. He has been awarded with Best Scientist of the year 2021. He is an industry speaker at multiple national and international conferences.
He holds a Master’s degree in management from IIM Ahmedabad and a Ph.D. in Computer Science & Engineering. He is a guide, researcher, and scholar, published 40+ national and international journals on Digital Transformations, Cyber Security, and Security Data Models. He has successfully driven several Industry-academia initiatives with various universities, tier-I, and tier-II technology institutions in Germany and India.
Dr. Vamsi holds several patents in secure data transmissions and cyber security. He is a Technology acumen across a wide range of digital technologies, including DevOps, Cloud, Cyber Security, Blockchain, RPA, AI/ML, and Cognitive engineering. His experience transformed from monolithic to microservices, and on-premise to cloud & edge computing.
Prior to Hub Technologies, Dr. Vamsi Mohan served in various senior engineering leadership roles driving Technology CoEs, Digital Innovation Centers in EMEA, and APAC regions. His experience spans across enterprise architecture, platform engineering, and technology solutions focusing on future-proofing business through digital transformations and delivering customer fit solutions.
Area of Expertise
Unlocking the Power of RAG & LLMs
This session will delve into the exciting world of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). I'll cover:
• Core Concepts: A concise overview of RAG and LLMs, including their key components, architectures, and use cases.
• Open-Source Frameworks
• Best Practices
• Real-World Applications
• The Future of RAG & LLMs
Key Takeaways:
• A solid understanding of RAG and LLM fundamentals and their applications.
• Knowledge of key open-source frameworks and their capabilities.
• Practical guidance on building and deploying effective RAG and LLM systems.
• Insights into the future of RAG and LLM technologies.
Target Audience:
• Developers
• Data Scientists
• Machine Learning Engineers
• Researchers
• Anyone interested in AI and machine learning
SaaS Microservices Architecture
The session "SaaS Microservices Architecture" helps to understand modern ways of developing SaaS products through microservices architecture. It is more responsive, resilient, and secure. Dr. Vamsi Mohan Vandrangi is going to discuss his thoughts, and share his experiences on transforming solutions to overcome traditional SaaS architectural methods.
Choosing Right Database for Your Microservices
Microservices are gaining popularity as infrastructure building blocks due to they provide benefits such as decoupling of services, data store autonomy, minimizing development and testing setup, and provide other advantages like faster time-to-market for new applications. One of the fundamental ideas of microservices design is the overcoming from a monolithic application framework, which supports data sharing across services over the use of a single large database.
A monolithic design lacks the flexibility and agility that a microservices design provides. On the other hand, having a dedicated database for each microservice may result in a polyglot system, which will inevitably become expensive and difficult to maintain over time.
Dr. Vamsi Mohan is going to speak on "Choosing a Right Database for your Microservices". He'll go through the best practices and the importance of taking into account non-functional requirements including performance, dependability, and data modeling. He will be presenting performance benchmarking of the Open-source databases for considering microservice-based applications.
Attendees can benefit from the session, choosing the right database for their enterprise-grade microservice applications.
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