April 15-17 software engineering teams will gather in the Bay area for the Second Annual QCon.ai Software Conference.
Designed as a machine learning conference for software engineers, QCon.ai is focused on helping teams adopt and validate roadmaps in machine learning by hearing and learning from those that are doing it today. What truly differentiates QCon.ai is its machine learning focus given through the lens of the software engineer. QCon.ai takes the AI/ML/DataEng tracks you find at the International software QCon and expands them into two full days of deep technical content (all targeted for a software engineer and technical engineering leadership).
While a few slots remain, the full conference schedule is available now and demonstrates the focus on the software. Some of the talks include:
- Massive Scale Anomaly Detection Framework
by Guy Gerson and Uri Silberstein (Cloud & Big Data Engineers @PayPal) - Deep Learning from a SWE's Perspective
by Sravya Tirukkovalur (Senior Machine Learning Engineer @Adobe) - People You May Know: Fast Recommendations over Massive Data
by Sumit Rangwala, and Felix GV (AI & DataEng Teams @Linkedin) - Developing a Machine Learning MVP @ThirdLove
by Megan Cartwright (Director of Data Science @ThirdLove) - Forecasting with Prophet
by Sean Taylor (Core Statistics Team @Facebook) - Improving Developer Productivity with Visual Studio Intellisense
by Allison Buchholtz-Au (Program Manager II @Microsoft) and Shengyu Fu (Principal Data Scientist Manager @Microsoft) - and more from Uber, Google, Cloudera, Airbnb, Slack, Confluent, Spotify
QCon.ai San Francisco Keynotes
All QCon.ai keynotes have been announced for the conference. These talks for the full audience cover everything from the very practical (updates and patterns with Keras/TensorFlow 2 and privacy/fairness) to the visionary (what the future of transportation will look like).
Best Practices for Maximizing Productivity in Deep Learning Research and Development
Francois Chollet, Software Engineer @ Google & the creator of Keras, discusses best practices for deep learning in the opening keynote for QCon.ai. The talk discusses techniques and recommendations for designing highly productive APIs for machine learning, and offers essential updates on the new TensorFlow 2.0 framework.
Privacy: The Last Stand for Fair Algorithms
In a world where big data is continuously touted as "the new oil" and U.S. companies are shutting their websites down rather than following increased European privacy rules, why should we care or worry about privacy? In this talk, Katherine Jarmul, Co-founder of KIProject, will discuss privacy for data science and why ensuring privacy for machine learning contributes to creating more ethical and fair models. She'll dive into research related to fair-and-private machine learning algorithms and privacy-preserving models, showing that caring about privacy and working to preserve user privacy in your machine learning workflows can help ensure a better model overall and support a more ethical product design.
In the evening keynote for QCon.ai, Dr. Sengupta, Aerospace Engineer, Chief Product Officer @AirspaceXP, Prof @USC, Pilot, ex @NASA Rocket Scientist (she led the team that designed the reentry system for the Mars Curiosity Rover) discusses the future of transportation with an eye towards how machine learning and AI are shaping our future. Expect autonomous, flying, zero-emission taxis, supersonic trains, and space to be on the agenda.
As a conference that doesn't cut corners or take shortcuts, QCon has a reputation for a premium no marketing, no fluff conference experience (NPS at the recently completed QCon San Francisco was 43%). This premier conference experience attracts high caliber attendees, and QCon creates space to help these senior software engineers and leaders network and connect, through:
- 20 Minute Breaks: "The Hallway Track"
QCon.ai is not a huge conference. All on one floor with all of the experience you expect from a QCon, the longer breaks allow attendees to mingle and network with other attendees, speakers, and sponsors. - Ask Me Anything Sessions with the Speakers.
AMAs (Ask Me Anything) are Q&A periods with groups of our speakers outside of the normal track boundaries. These are unrecorded, small group sessions with sets of speakers around a topic. Reinforcement learning, transfer learning, deep learning, and building machine learning capabilities. AMAs are opportunities to connect with speakers and learn from their journey in ML. - Evening Keynote at the End of Day 1 from Anita Sengupta
Day one of QCon.ai opens with the creator of Keras/software engineer @Google discussing best practices for machine learning and the upcoming changes with TensorFlow 2.0 and Keras. After a full day of deep technical sessions geared for SWEs, the day ends with aeronautics engineer Dr. Anita Sengupta (Chief Product Officer working on Electric Autonomous VTOL Air Taxis, and who has designed Mars Curiosity Rover's supersonic parachute, and worked on the Cold Atom Lab for the International Space Station, & Virgin Atlantic's Hyperloop) talking about the future of transportation. This talk will inspire and provide context for what an AI future will hold.
The cost to attend the two-day conference is $1,935 (group discounts are also available by emailing info@qcon.ai). While $1,935 is fully inclusive of the three day conference (read: no additional charges for anything within the conference), a pre-conference workshop day is available stand-alone or bundled with the conference (optional workshops are available on Deep Learning with PyTorch, Anomaly Detection, Apache Kafka / KSQL, and Recommendation Systems with Spark 2).