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.
Evolution of data science roles in the age of low-code tools and LLMs
Brief : Beyond the analyst,scientist and ML engineer roles, what are the possible evolutions of roles surrounding data science and how we are seeing it develop across different projects in our agritech startup .
- Possible ways of structure a data collection, engineering and science team and pros and cons of them
- Walkthrough various low-code tools can be used to empowered roles that did not have access to or ability to build software earlier.
525 million smallholder farmers grow 70% of the world’s food, yet they’re among the poorest people on earth. We’re changing that.
- Credit models that predicts default probability of short term loans disbursed for financing crop procurement
- Crop doctor - plant disease detection model that can be used by farmers
- LLMs that enable the access of agronomy advise to indonesian farmers
- data annotators (knowledge worker), active learning , users
- data quality filtering using reviewers
- focused labelling using active learning
- data quality assessment
- data quality maintainers and processes to created to evaluate
- explainable models,compliance , Relationship managers who need to be model explainers
- How sql and low code tools have empowered teams on the ground to create and maintain their own data processes and pros/cons around them