Speaker
Abstract
What is an unconference?
At QCon SF, we’ll have unconferences in most of our tracks. An unconference is a simple way to run productive, structured conversations for 5 to 2000 or more people, and a powerful way to lead any kind of organization in everyday practice and extraordinary change. Our unconference sessions are based on the Open Space Technology and Lean Coffee.
Why are we doing unconference sessions?
We’re doing unconferences at QCon because we want this conference to be yours. At QCon, we learn from the best and share with the best. We come with passions and ideas that we want to share with each other. We want to connect with each other, create community around topics that we’re passionate about. We do that with unconferences.
How do open space sessions work?
The Law of Two Feet means you take responsibility for what you care about -- standing up for that and using your own two feet to move to whatever place you can best contribute and/or learn.
Four principles apply to how you navigate our open space sessions:
- Whoever comes is the right people. Whoever is attracted to the same conversation are the people who can contribute most to that conversation—because they care. So they are exactly the ones—for the whole group-- who are capable of initiating action.
- Whatever happens, is the only thing that could've. We are all limited by our own pasts and expectations. This principle acknowledges we'll all do our best to focus on NOW-- the present time and place-- and not get bogged down in what could've or should've happened.
- When it starts is the right time. The creative spirit has its own time, and our task is to make our best contribution and enter the flow of creativity when it starts.
- When it's over, it's over. Creativity has its own rhythm. So do groups.
What’s next?
Bring your passion and ideas to QCon unconference. See you there!
Interview
QCon San Francisco 2022 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
MLOps Hosted by Hien Luu AI/ML Leader, Advisor, Speaker, and AuthorFrom the same track
Monday 24 October
10:35 Pacific DEKJ Session Machine Learning Ray: The Next Generation Compute Runtime for ML Applications Zhe Zhang Head of Open Source Engineering @anyscalecompute, Previously Hadoop/Spark infra Team Manager @LinkedIn Ray is an open source project that makes it simple to scale any compute-intensive Python workload. Industry leaders like Uber, Shopify, Spotify are building their next generation ML platforms on top of Ray. 11:50 Pacific DEKJ Session Machine Learning Fabricator: End-to-End Declarative Feature Engineering Platform Kunal Shah ML Platform Engineering Manager @DoorDash, Previously ML Platforms & Data Engineering frameworks @Airbnb & @YouTube At Doordash, the last year has seen a surge in applications of machine learning to various product verticals in our growing business. However, with this growth, our data scientists have had increasing bottlenecks in their development cycle because of our existing feature engineering process. 13:40 Pacific DEKJ Session Machine Learning An Open Source Infrastructure for PyTorch Mark Saroufim Applied AI Engineer @Meta In this talk we’ll go over tools and techniques to deploy PyTorch in production. The PyTorch organization maintains and supports open source tools for efficient inference like pytorch/serve, job management pytorch/torchx and streaming datasets like pytorch/data. 14:55 Pacific DEKJ Session Machine Learning Real-Time Machine Learning: Architecture and Challenges Chip Huyen Co-founder @Claypot AI, previously @Snorkel Ai & @NVIDIA Fresh data beats stale data for machine learning applications. This talk discusses the value of fresh data as well as different types of architecture and challenges of online prediction. 16:10 Pacific DEKJ Session Machine Learning Declarative Machine Learning: A Flexible, Modular and Scalable Approach for Building Production ML Models Shreya Rajpal Founder @Guardrails AI, Experienced ML Practitioner with a Decade of Experience in ML Research, Applications and Infrastructure Building ML solutions from scratch is challenging because of a variety of reasons: the long development cycles of writing low level machine learning code and the fast pace of state-of-the-art ML methods to name a few. 17:25 Seacliff D Session Unconference: MLOps Shane Hastie Global Delivery Lead for SoftEd and Lead Editor for Culture & Methods at InfoQ.com What is an unconference? At QCon SF, we’ll have unconferences in most of our tracks.