AI & Research,And Industrial Tracks - all videos, Also inviting registrations for Signal In Bangalore This update is for participants only
There are three phases in the lifecycle of an application - research, application and aftermath of the application.
- Assess capabilities, determining the new frontiers for AI.
- Find a use for the application.
- Learn how to run it, monitor it and update it with time.
The three tracks at the 2023 Monsoon edition of The Fifth Elephant will cover this lifecycle.
The 2023 Monsoon edition is curated by:
- Nischal HP, Vice President of Data Engineering and Data Science at Scoutbee. Nischal curated the MLOps conference which was held online between 23 and 27 July 2021.
- Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.
- AI and Research - covers research, findings, and solutions for challenges on building models in various areas such as fraud detection, forecasting, and analytics. This track delves into the latest methodologies for handling challenges such as large-scale data processing, distributed computing, and optimizing model performance.
- Industrial applications of ML - covers implementation of AI in the industry, with more focus on the AI models, the issues in training, gathering data so, and so forth. ML is being used at scale in industries such as automotive, mechanical, manufacturing, agriculture, and such domains. This track focuses on the challenges in this space, as we see innovation coming out of these industries in the pursuit of using ML on a second-to-second basis.
- AI and Product - covers strategies for building AI products to scale and mitigating challenges. This track provides insights on incorporating AI tools and forecasting techniques to improve model training, developing a working model architecture, and using data in the business context.
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Purchase a membership to attend the conference in-person. If you have questions about participation, post a comment here.
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies seeking tech branding among AI and ML developers.
- Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
An Asset Management Perspective of LLM for question answering- major challenges and opportunities
LLMs have been demonstrated to perform quite well in question answering tasks and have been shown to generate good answers based on the context provided. In many scenarios, training of LLM becomes challenging due to time and resource constraints. When it comes to adoption of LLM in large organizations, major problem arises due to the confidentially of data and scattered relevant information for question answering tasks. The problem becomes more severe because the relevant information is present among multiple files which are much larger in length as compared to the maximum context length of major LLMs.
In Asset Management, Research Analysts/Portfolio Managers need to go through large number of research reports both private and publicly available and need to extract relevant information and compare across companies, sectors, years, markets etc. We propose a solution to this problem where the user can do question answering with their reports which can be of different format, can vary from few to thousands in number and can be of varying sizes. We present some of our learnings while solving problems like handling of limited context window, utilizing information like metadata effectively, chunking strategies etc.
What will you get out of this talk:
• Deep dive into LLMs and prompt engineering
• Strength and limitations of LLMs for question answering tasks
• An Asset Management perspective on LLMs
Kunal Satija : Fidelity Investments
Pradeep Rathore : Fidelity Investments