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Avinash Pathak

Senior AI Engineer

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Avinash Pathak, Senior AI Engineer at NVIDIA, specializes in LLM-based applications such as multimodal chatbots and GUI generation. With expertise spanning NLP and Large Language Models (LLMs), including seq2seq, LSTMs, BERT, and XLNET, he has also contributed to vision tasks like object detection and retail data analytics, developing models for the likelihood of buying and paying total price. His role at NVIDIA underscores his proficiency in cutting-edge AI technologies and his ability to innovate across diverse domains, exemplifying his commitment to advancing the field of artificial intelligence.

In this engaging session, we will explore the world of large language model (LLM) agents and sophisticated AI systems that extend the capabilities of standard language models to execute specific, complex tasks. We will demystify the core components that define LLM agents: the Agent Core, Memory Systems, Tools, and Planning Module, detailing how these elements integrate to enhance decision-making and task execution.

We will explore a range of practical applications of AI agents in finance, demonstrating how LLM agents can help you find and analyze financial data for you. For instance, If you want to see today's market trends and news affecting the stock market, you can just run your agent, and you will get the information immediately. 

The session's highlight will be a live demonstration, during which attendees will see an LLM agent. This demonstration will focus on a simulated scenario where agents operate and work with financial data.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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