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.
Navigating the Credit Seas: A Unified Framework for Credit Risk Modeling in CPG Industry
The Consumer Packaged Goods (CPG) industry faces unique credit risk challenges such as fluctuating consumer demands, the risk of bad debt, optimal working capital management, and market volatility.
These challenges necessitate a robust and dynamic credit risk model to accurately assess and manage credit risks. While similar problems have been addressed in the banking sector, the CPG industry presents unique challenges due to its specific market dynamics and consumer behavior patterns. The integration of AI and machine learning in this context, while ensuring data privacy and governance, presents a unique problem to solve.
The proposed solution is a comprehensive AI-driven framework for credit risk modeling specifically designed for the CPG industry. This framework leverages Decision Intelligence Systems and Responsible AI principles to provide a step-by-step guide for building a robust credit risk model.
It emphasizes the integration of cutting-edge technologies such as machine learning and big data analytics to enhance predictive accuracy and decision-making agility. The framework also highlights the importance of model governance, monitoring, and maintenance to ensure the model’s effectiveness over time. Furthermore, it underscores the need for regulatory compliance, data security, data privacy, and ethical considerations in credit risk assessments, focusing on transparency, fairness, and model explainability.
Without a comprehensive AI-driven credit risk model, CPG companies may face a multitude of risks. The most significant of these is financial risk, as companies may struggle to maintain financial stability and sustainability without accurate credit risk assessments.
This could lead to issues such as bad debt and challenges in managing optimal working capital. In addition, companies may also face operational risks if they lack a robust system for credit risk management. This could result in inefficiencies and errors in credit decisions, potentially leading to financial losses.
Strategy risk is another implication, as companies without a dynamic credit risk model may fail to adapt their strategies to changing market conditions, potentially leading to missed opportunities or financial losses. Legal risk is also a concern, as companies that fail to comply with regulatory standards in credit risk assessments could face legal repercussions.
This could also lead to reputation risk, as non-compliance with regulations or unethical practices in credit risk assessments could damage a company’s reputation, potentially leading to loss of customers or business partners.
Introduction to Credit Risk: Understanding the unique credit risk challenges in the Consumer Packaged Goods (CPG) industry and the need for AI and machine learning solutions.
Credit Risk Playbook: A step-by-step guide to building a credit risk and credit limit model, starting with data collection, preprocessing, feature selection, model development, validation, and backtesting.
Model Explainability and Stability: Exploring the importance of model explainability, ensuring that stakeholders understand how the model works and makes decisions, and emphasizing transparency in credit risk assessments along with the stability of credit risk scores.
Experiment Design: Discussion on how to design experiments to test the model’s performance under a variety of scenarios.
Model Deployment (MLOps): Discussing the final step of the process, deploying the model into the real world, and using it to make credit risk assessments.
Model Governance, Regulatory Compliance, and Ethics: Highlighting the importance of model governance, monitoring, and maintenance, and emphasizing the need for adherence to regulatory standards and ethical considerations in credit risk assessments, focusing on fairness in credit decisions.
Speaker Linkedin Profile: https://www.linkedin.com/in/mraduljain1/