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.
Synthetic Sorcery: Fooling Neural Networks with Unreal Data for Real-World Applications
The integration of computer vision technologies in the automotive industry has revolutionized various aspects of vehicle safety, navigation, and driver assistance systems. However, developing robust and accurate computer vision models for real-world scenarios necessitates large-scale, diverse, and accurately labeled datasets, which can be challenging to obtain. Consider a scenario where the duration of data collection and annotation efforts, spanning from 60 to 90 days and involving hundreds of manual annotators, is condensed to a mere 2 hours of effort resulting in impeccable, error-free data.
Drawing on our group’s experience at Mercedes-Benz R&D India in research as well as delivering customer-ready products in the area of intelligent interior and in-cabin sensing (MBUX interior assist), this talk will explore the benefits of using synthetic data to augment or replace traditional, labor-intensive data collection methods.
We will delve into the techniques and methodologies employed for generating synthetic data tailored to computer vision tasks. This includes modeling virtual environments, capturing complex actions, and incorporating accurate ground truth annotations.
Furthermore, we will examine the key challenges involved in using synthetic data that closely matches real-world data distributions and the strategies used to bridge the domain gap between synthetic and real data. This talk will highlight successful applications of synthetic data in automotive domain. Specifically, we will focus on the techniques we employed on datasets like cityscapes, achieving state-of-the-art results (Top-1 when submitted, now Top-3).