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. 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.
The Journey of Machine Learning Platform at Myntra
Myntra is one of the leading fashion e-commerce companies in India. Myntra is focused on delivering best-in-class customer experience for all the fashion lovers from browsing to purchase and post purchase experience. Myntra provides curated, customized shopping experience to every user by matching deep understanding of the user with deep expertise on fashion and trends.
Myntra is leveraging AI and ML to solve complex problems on various facets of e-commerce operations - store front, user shopping experience, supply chain (inbound and outbound), pricing etc. and use cases continue to expand. Myntra deployed and continues to deploy many advanced machine learning models to solve critical business problems in these areas.
Machine learning lifecycle - getting the right datasets, model training, deployment, serving (online and offline), integration with services, model health monitoring and alerting is extremely complex. This requires data science, data engineering, ML engineering, software engineering and MLOps practices to come together. There is no single or set of open source or 3rd party tools that provide the capabilities required to manage the end to end ML lifecycle for Myntra scale.
Myntra Engineering team designed and built an in-house Machine Learning Platform, with the goal to support the entire machine learning lifecycle from model development to deployment, serving, model health, feedback loop and integration with applications. The machine learning platform is scalable to support online/offline / batch serving patterns and provides self serve, CICD and MLOPs capabilities.
Outline of the talk:
The Lifecycle of a Machine Learning model
Role of Machine Learning Platform at Myntra
Design, Architectural principles and Tech choices
Deep dive into the platform capabilities
Overview of an online serving use case deployed on the platform
Best practices and learnings
This talk will be jointly presented by Narayana Pattipati(Senior Architect, Myntra Data & Machine Learning Platforms) and Meghamala Ulavapalle(Senior EM, Machine Learning Platform)