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Unconference: Modern ML
What is an unconference?
An unconference is a participant-driven meeting. Attendees come together, bringing their challenges and relying on the experience and know-how of their peers for solutions. A professional facilitator is also there to help keep the discussion moving forward, but where it goes is up to the participants.
It's a facilitated peer group that avoids the hierarchical aspects of a conventional conference, such as a top-down organization. Only the broad themes are predetermined. Everything else is just space for attendees to sound off ideas together, relate to shared challenges and rewards, and identify new ideas and goals.
Our unconference sessions have been based on the Open Space Technology and Lean Coffee format since 2006.
Why are we doing unconference sessions?
We have designed QCon for senior software practitioners. That role comes with demanding challenges and complex problems.
Connecting with your peers in a structured environment allows you to:
- Broaden your perspective with the benefit of the experience of others.
- Challenge how you've been doing things by breaking out of your bubble.
- Learn from peers who have already overcome the challenges you're facing now.
- Benchmark your solutions against other teams and organizations.
- Get real-world perspectives on challenges that might be too novel or specific to find solutions in books or presentations.
- Validate your technical roadmap with real-world research.
- Connect with others like you and build relationships that go beyond the event.
From the same track
Session
AI/ML
Chronon - Airbnb’s End-to-End Feature Platform
Tuesday Oct 3 / 10:35AM PDT
ML Models typically use upwards of 100 features to generate a single prediction. As a result, there is an explosion in the number of data pipelines and high request fanout during prediction.
Nikhil Simha
Author of "Chronon Feature Platform", Previously Built Stream Processing Infra @Meta and NLP Systems @Amazon & @Walmartlabs
Chronon - Airbnb’s End-to-End Feature Platform
Session
AI/ML
Defensible Moats: Unlocking Enterprise Value with Large Language Models
Tuesday Oct 3 / 11:45AM PDT
Building LLM-powered applications using APIs alone poses significant challenges for enterprises. These challenges include data fragmentation, the absence of a shared business vocabulary, privacy concerns regarding data, and diverse objectives among data and ML users.
Nischal HP
Vice President of Data Science @Scoutbee, Decade of Experience Building Enterprise AI
Defensible Moats: Unlocking Enterprise Value with Large Language Models
Session
Distributed Computing
Modern Compute Stack for Scaling Large AI/ML/LLM Workloads
Tuesday Oct 3 / 01:35PM PDT
Advanced machine learning (ML) models, particularly large language models (LLMs), require scaling beyond a single machine.
Jules Damji
Lead Developer Advocate @Anyscale, MLflow Contributor, and Co-Author of "Learning Spark"
Modern Compute Stack for Scaling Large AI/ML/LLM Workloads
Session
AI/ML
Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)
Tuesday Oct 3 / 02:45PM PDT
In this presentation, we will delve into the world of Retrieval Augmented Generation (RAG) and its significance for Large Language Models (LLMs) like OpenAI's GPT4. With the rapid evolution of data, LLMs face the challenge of staying up-to-date and contextually relevant.
Sam Partee
Principal Engineer @Redis
Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)
Session
AI/ML
Building Guardrails for Enterprise AI Applications W/ LLMs
Tuesday Oct 3 / 05:05PM PDT
Large Language Models (LLMs) such as ChatGPT have revolutionized AI applications, offering unprecedented potential for complex real-world scenarios. However, fully harnessing this potential comes with unique challenges such as model brittleness and the need for consistent, accurate outputs.
Shreya Rajpal
Founder @Guardrails AI, Experienced ML Practitioner with a Decade of Experience in ML Research, Applications and Infrastructure
Building Guardrails for Enterprise AI Applications W/ LLMs
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