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.
Prototype to Production in a day: the new era of AI
The last 12 months have seen a relentless pace of innovation in the field of Artificial Intelligence (AI). Each week hundreds of new models and code repositories are released. It’s clearly a wonderful time to be working in the field but the volume and pace do present their own unique challenges. How to develop products that support rapid prototyping whilst frequently changing the models powering the experience? How to quickly take a newly open-sourced model from initial discovery to production in the hands of the users?
At Glance we provide our users (225m) with an AI powered smart lock screen experience featuring personalised premium content across a range of genres including, casual gaming, LIVE game streaming, news, sports, lifestyle, fashion, entertainment, creator-led LIVE content and more. One of the key applications of AI at Glance involves transforming content from one format such as a text article into a rich interactive video. The wave of innovation in Generative AI has had a huge positive impact on this work.
In our talk we will share the lessons learned whilst building LEX – our Content Generation System. We will discuss how this how the design patterns we apply have evolved from the more traditional. ‘Gather requirements for the new feature we may build, debate, define rigid specification, implement, test, deploy’ to a more liquid approach with Machine Learning Engineers deploying new features that they are in effect sponsoring (championing themselves). This required novel approaches to the UX design and complete decoupling of the ML models powering LEX. Model can then be replaced with superior versions as they become available. This allows upgrades such as GPT 3.5 to version 4.0 to occur within minutes. The net effect is LEX continues to grow in terms of performance and functionality with limited developer intervention.
- The Challenge of Rapid Innovation in AI
- Exploring the fast-paced landscape of AI innovation and its unique challenges
- Adapting to the constant release of new models and code repositories
- Strategies for swiftly incorporating new models into the content generation system
- Empowering Content Generation with Generative AI
- Leveraging generative AI techniques to create diverse content formats such as blogs, stories, videos, and quizzes
- Exploring the impact of generative AI on content quality, diversity, and personalization
- Use cases and examples of how generative AI enhances Glance’s content offerings
- Transforming Content Curation to AI-driven Creation
- Evolution of the content team’s role from curating existing content to actively creating it using AI tools
- The collaborative workflow between the content team and AI systems like LEX
- Benefits and challenges of AI-driven content creation, including scalability and quality control
- Not committing to any one model
- Decoupling models and it’s usage helps with moving at a pace at which AI is involving. Be it LLMs or image models
- Understanding the importance of quickly adapting to new AI models and technologies in content generation systems
- Exploring the potential of generative AI in creating diverse and hyper personalized content in different formats
- Lessons learned in managing the collaborative workflow between human content creators and AI systems