The Fifth Elephant 2023 Winter edition will cover topics on the research, engineering, and business aspects of AI, exploring the practical implementation and economic implications of these systems.
In 2020, OpenAI released a Large Language Model (LLM) called GPT3 which has a billion parameters. With a minimal and intuitive user interface which was released to go with GPT3, it caught the imagination and attention of AI communities and researchers all over the world.
One by one, the domain use cases such as co-pilots for coding, creative AI, and other downstream tasks were shown to be fast-tracked by GenerativeAI models and LLMs. As such, there is a wide-ranging interest in large language models and applications around them for various domains and use cases in the AI space. Experiments which aim to find optimal hyperparameters, and those dealing with underfitting and overfitting models are being carried out regularly; more and more barriers are being broken down every day.
The winter edition of The Fifth Elephant will showcase talks, discussions and demos across generative and multimodal AI, and other classic AI/ML/DL applications on the below themes.
Share approaches and case studies covering the following use cases:
- Products and platforms using LLMs, GenerativeAI, ML, and Deep Learning techniques, and business formulation around AI engineering.
- Conversational AI and search, automatic speech recognition, healthcare, e-commerce, fintech, media and OTT, and other verticals.
- Multilingual needs in India in digital products/platforms - features discussions, models training, finetuning, RLHF, RAGs, quantization techniques, dataset curation and augmentations, challenges faced in pipelines, evaluation metrics, future roadmaps, applications such as multilingual voice bots using ASR/STT, text to speech for accessibility.
Share case studies and experiential talks on handling the operations for data science such as scaling challenges and fine-tuning challenges, and lessons learned, and best practices for incorporating ethics, safety, and bias.
Show demos on features/products which leverage AI and LLM-based APIs and models. It can be from creative AI, generative AI space, and various verticals with relevant use cases.
The December edition will be held in-person. Attendance is open to The Fifth Elephant members only. Pick a membership to attend the in-person conference, and to support The Fifth Elephant’s community activities.
- AI/ML/Data Science Ops engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as health care, e-commerce, automobile, agri-tech and industrial verticals
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders and CTOs who are building products and platforms that leverage AI, ML and LLMs
- 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 who want to do 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.
If you are interested in sponsoring The Fifth Elephant, email firstname.lastname@example.org.
Scaling Recommendations @ Meesho: Lessons from Twitter, Instagram and Pinterest Ranking & Retrieval Strategies
In this session, we’ll deep-dive into Recommendation Systems, with a special focus on two critical stages: Retrieval and Ranking. We’ll begin by examining how different industries implement large-scale Retrieval Systems tailored to their specific needs. To illustrate our points, we’ll take a closer look at the Retrieval Systems used by Twitter and Instagram, even though both are social networks, their Recommender Systems are unique. We’ll explore Twitter’s follow suggestions and how Instagram recommends interactive media content to its users. We’ll uncover the strategies these companies use to create effective Retrieval Systems and identify common approaches.
Next, we’ll shift our attention to Ranking Systems, where we’ll explore how companies strategically order items to maximize conversions. We’ll discuss the use of multi-objective ranking functions, a common approach in many companies. Additionally, we’ll also examine how other companies fine-tune their rankings using feedback loops to align with multiple objectives. In this context, we’ll take a closer look at the ranking systems of Instagram and Pinterest.
Throughout this session, we aim to provide insights into the world of Recommendation Systems and how in Meesho we have used some of these learnings to create high-quality Recommender Systems.
About the Speaker
Abhishek Mungoli is a seasoned data scientist with over 7 years of experience, holding a master’s degree in Computer Science from IIIT-Hyderabad. He has worked with prominent companies such as Walmart and currently serves as a Lead Data Scientist at Meesho. Abhishek’s expertise spans various domains of data science, including supply chain, pricing, fraud analytics, recommendation systems, and advertising platforms. He is a thought leader in the field, regularly sharing his insights on platforms like LinkedIn and Medium, as well as through his YouTube channel, DataTrek. Abhishek has also delivered guest lectures at prestigious institutions like IIT Madras, Symbiosis Pune, Jindal University , and IIIT Sricity. He has build a modest following within the Data Science community, with around 19k+ followers on LinkedIn and approximately 3.7K+ subscribers on YouTube. Outside of his professional endeavors, he’s a fitness enthusiast and a devoted MMA fan.