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.
Demystifying Quantisation in Large Language Models in Plain English with Basic Math
- I will cover some basic maths of sizing up the memory and compute requirements of training and inference of a large language model. Some popular open source models will be used as example.
- Quick brush up of data types.
- In plain English, how are popular quantisation methods working?
- Take an example of a typical computation in a neural network and show what quantisation brings to the table.
- What impact does this make on compute, memory requirements? What is the fine print?
- Why is this important? How can you apply this in your work?
Quantisation has emerged as a significant enabler for large language models (LLMs), making them accessible for companies without extravagant budgets (read: throw money at the problem) and paving the way for edge deployments. This talk delves beyond the basic concept of converting floats to integers. I’ll explain the underlying math that governs the memory and computation requirements, demonstrating how quantisation computations facilitate not only inference but also, potentially, training. Additionally, I will illuminate the cost, computational, and business impacts of quantisation.
- Intuitive yet in-depth comprehension of why quantisation is crucial for training or fine-tuning LLMs.
- What is, roughly, happening in the maths? Where are the trade-offs?
- How does it impact accuracy? What is the evidence for its claims?
- How to make informed quantisation trade-offs, equipping them to exploit LLMs across various use cases effectively.
I have in past hosted many talks on ML/AI at Fifth Elephant and other conferences. These includes hands on workshops and short talks. I’m obsessed about giving a clear understanding of underlying maths fundamentals while also explain the business impact.