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.
Unleashing Innovation: Streamlining ML Experimentation with Alchemist
Glance inspires consumers to make the most of every moment by surfacing relevant experiences for them with its ‘smart lock screen’ innovation. More than 225 million consumers enjoy Glance on their Android smartphones across markets. Glance harnesses the power of Machine Learning (ML) to provide consumers with a highly personalized and engaging user experience featuring top content from both local and global publishers across a diverse range of topics. The key to delivering this experience has been a commitment to rapid and continuous experimentation. Enabling this is Alchemist - our state-of-the-art in-house experimentation platform.
When building Alchemist we had to take several unique design decisions to fulfill our goal of massive concurrent experimentation. For example, we needed to shift away from hash-based experiment targeting to a richer model capable of targeting any combination of user attributes with full experimentation lifecycle management. We had to develop a high-performance config server for experiment delivery across a vast user base that switched between offline and online states. We introduced automatic metric generation tied to the platform for seamless performance analysis. These core features of Alchemist have boosted the rate of ML experimentation, enabling us to test more features for our users and rapidly scale those they love.
In this talk, we will share our insights and lessons learned from developing and running Alchemist and how these can be applied to boost the rate of experimentation in your products and services.
This talk navigates the ambitious task of executing rapid experimentation at an immense scale. We explore the challenges that come with such a large scale, from ensuring personalization policies are honoured to maintaining performance, and the innovative solutions we’ve developed to meet these challenges head-on.
The cornerstone of our solution is Alchemist, our in-house experimentation platform. Alchemist features a sophisticated engine capable of targeting any combination of user attributes with full experimentation lifecycle management, a high-performance config server for the user base, and a unified metric bed that ensures verified metrics are accessible in a single place. These features alongside other aspects of Alchemist have increased our speed of experimentation; accelerating the pace of innovation.
We discuss the business implications of Alchemist, shedding light on how it empowers Product Managers, Engineers, and other stakeholders to conduct massive concurrent experimentation. With Alchemist, the typically time-consuming process of setting up experiments becomes swift and efficient, all consolidated within a single portal, providing out-of-the-box metrics.
The impact of Alchemist transcends beyond ML experimentation, its versatility makes it a powerful tool for experimenting with any aspect of the user experience - be it UX, personalization, or backend modifications.
Join us as we explore the dynamic world of rapid experimentation enabled by Alchemist. Learn how you can apply these core learnings and principles to your own experimentation practices.