Session on "Use Cases and Risks of ML in Capital Markets" | 23rd Dec at 4pm Hi everyone! The AI and Risk Mitigation project is well underway and for the third session, we will be joined by Rachna Maheshwari, Associate Director at CRI… more
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.
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 Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Pick a membership to attend the in-person conference. 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.
Near Real time feature engineering at scale for machine learning use cases at Myntra
Myntra is one of the leading fashion e-commerce companies in India. Myntra delivers best-in-class shopping experience by leveraging many advanced machine learning models, deployed for online or real-time inference. The online inference requires streams of data to be processed, machine learning features to be computed, stored and served in (near) real-time, at Myntra scale.
The features can be hand crafted or generated (e.g. user, product, style, widget, image embeddings). And majority of the features require stateful stream processing with complex computation, in (near) real-time, at very high throughputs (millions of rpm) and low latency. This requires scalable, resilient data engineering systems with stateful stream processing capabilities and feature stores.
Myntra Data Engineering team designed and built Quicksilver, a real time data ingestion and stateful stream processing platform. It is part of the overall Myntra Data Platform. The Quicksilver platform ingests millions of events every minute, computes the machine learning features in (near) real time and makes them available to machine learning models for online inference.
- Online ML use cases at Myntra
- Life cycle of an online ML model, including feature engineering
- Challenges of realtime feature engineering at scale
- Functional and non-functional requirements
- Architecture of the QuickSilver platform, design principles and tech choices
- Integration with Machine learning platform
- Best practices and learnings
Aditya S ( Tech lead, Quicksilver Near Realtime Platform )
Narayana Pattipati ( Senior Architect, Myntra Data & Machine Learning Platforms)