High quality talks from the innovators in the industry with no sales pitch. Helped me to get a much broader and deeper understanding on where AI is headed and some of the areas where it is actively applied.
2019 Tracks
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Solving Software Engineering Problems with Machine Learning
Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
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Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.
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Groking Timeseries & Sequential Data
Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.
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Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.
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AI Meets the Physical World
Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.
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Papers in Production: Modern CS in the Real World
Groundbreaking papers make real-world impact.
Speakers
Keynotes
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? Is privacy dead? If not, should we work to preserve it? In this talk, we'll dive into privacy for data science and why ensuring privacy for machine learning contributes to creating more ethical and fair models. We'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.
Co-Founder of KIProtect
Best Practices for Maximizing Productivity in Deep Learning Research and Development
Francois Chollet, 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.
Software Engineer @Google & Creator of Keras
The Future of Transportation
In the evening keynote for QCon.ai, Dr. Sengupta discusses the future of transportation with an eye towards how machine learning and AI will help shape our future. Dr. Sengupta is an aerospace engineer, rocket scientist, and veteran of the space program. She worked for NASA for 16 years where her engineering projects included her PhD research on developing the ion propulsion system for the Dawn Mission (currently in the main asteroid belt), the supersonic parachute that landed the Curiosity rover on Mars, and the Cold Atom Laboratory an atomic physics facility now on board the International Space Station. After leaving NASA she led the development of the hyperloop as senior vice president of engineering systems at Virgin Hyperloop, a technology that can enable ground based travel in excess of airline speed. Her current engineering adventure is designing electrified autonomous VTOL air taxis for urban aerial transport, as Chief Product Officer and Vice President of Business Development at Airspace Experience Technologies. As an engineering savvy executive and pilot, she is now leading the mobility solutions for smart cities by eliminating congestion and reducing the carbon footprint of air travel.
Aerospace Engineer, Chief Product Officer @AirspaceXP, Prof @USC, Pilot, ex @NASA Rocket Scientist
Featured Presentations
"Privacy" by Owen Moore is licensed under CC BY 2.0.
Canopy is "taking a new approach to personalized discovery. One that doesn’t require knowing every action you take or every preference you have." Erica Green, a machine learning engineer from Canopy, will be discussing their approach to privacy during QCon.ai. She'll discuss their recommendation stack along with issues around analytics and algorithmic fairness.
Early detection of abnormal events can be critical for many business applications; however, there are numerous challenges when implementing real-time anomaly models at scale. Hear Uri Silberstein (Senior Cloud & Big Data Developer @PayPay) and Guy Gerson (Big Data Developer @PayPal) discuss topics around server failure, developer error, and malicious activities when building an anomaly detection framework at scale. This talk features lessons learned building on top of Spark Structured Streaming's fast execution engine.
Workshops
QCon.ai San Francisco Venue & Hotel
Parc 55 San Francisco
Parc 55 San Francisco - A Hilton Hotel
This contemporary, high-rise hotel is 1 block from Union Square with stunning views all around and close to popular attractions, events, and shopping. The conference venue is at the same location as the hotel.
Address
Parc 55 San Francisco – a Hilton Hotel
55 Cyril Magnin Street
San Francisco, California
94102 USA
Tel: +1-415-392-8000
Reservations
Stay at the QCon.ai venue
Special rate for attendees
(Possibility of early sell-out)
Is QCon.ai Right for You?
Our attendees roles are:
Software Developer / Programmer/ Engineer
Senior Developer / Engineer
Technical Team Lead and Higher (including):
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Technical Team Lead
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Senior Management (VP, CTO, CIO, Director)
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Architect: Technical / Application (platform specific)
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Enterprise Architect / Chief Architect
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Architect: Solution / Systems
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Technical Project Manager
Meet and Learn from Your Peers
20 Minute Breaks “The Hallway Track”
Mingle and network with other attendees, speakers & sponsors.
Ask Me Anything Sessions with the Speakers
AMAs (Ask Me Anything) are Q&A periods with our speakers outside of the normal track boundaries. AMAs are one more opportunity to connect with speakers and learn from their journey.
The People Behind The Conference
Speakers for QCon.ai are invited by our committee of industry practitioners. This is our amazing committee
Bleeding-edge for the Enterprise
Bring trends from innovator and early adopter companies home to your team
Private On-device Recommendation
Deep Learning
Unsupervised Learning
Self-Driving Vehicles
Transfer Learning
Reinforcement Learning
Machine Learning Model Interpretability
Multi-Task Learning
Stream Processing
Chatbots
Recommendation Engines
Lambda/Serverless
Recommendations / Personalization / Relevance
Tensorflow
NLP /NLU
Sequential Data
Fairness / Privacy
DevOps
Containers
Spark Streaming
Kafka
Jupyter Notebooks
R / Python Use Cases & Tips
Why QCon.ai
Why Senior Developers, Architects, and Software Engineers are looking for AI and Machine Learning Topics geared towards them?
QCon.ai: Applied AI for software engineers rather than data scientists
Sander Mak
Sr Software Engineer at Luminis Technologies
Machine learning is one of the trends that need your attention. We’ve all heard about deep learning and the cool stuff Google is doing with it, but I think that enterprise applications product owners will be asking for more of these features. While it’s not trivial to get into, I think machine learning is really a skill set that software engineers should acquire now.
Felipe Huici
Chief Researcher, Systems & ML at NEC Laboratories
What I see as a major trend is that a lot of things (not everything, but alot of them) are going to become more and more driven by machine learning algorithms. If you care about your resume, it is going to look nice to say I have machine learning experience.
Haley Tucker
Software Engineer, Netflix
What I’d like to look more into and spend more time on is the machine learning space. I keep running into problems in my current job that just feels like there is a machine learning solution to it. I think there is alot of value in spending time in that space.
Software is changing the world.
AI is changing software
Software development is always evolving. And Software engineers continue to evolve with it.
Before DataEng became a thing, we had DBA’s and ETL folks. Software Engineers became more involved with the work and created the DataEng field. Before DevOps, we had Operations/Systems Administrators. Software Engineers became more involved with the work and created DevOps. We are seeing the same thing happen in SecOps… security folks who have operational SE skillsets.
Now, AI and machine learning are changing and shaping the future of software. Traditionally, this has been the field for PhD level data scientists. But as tooling and libraries are becoming more available and understood, that’s changing. Software engineers are moving into this field creating new roles, such as Machine Learning Engineers.
Our hypothesis is that there are large numbers of software engineers who have the talent to harness data in how they work, but don’t know the right problems to solve with AI and machine learning in engineering. When should you use a machine learning algorithm? When is a rules engine the right approach? At QCon.ai, we’ll help senior software engineers and architects uncover the real-world patterns, practices, and use cases for applying artificial intelligence/machine learning in engineering.
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10 Years of QCon and InfoQ, now bringing you an AI-focused conference
QCon has been delivering a platform for senior developers, engineers, & leaders to discuss software engineering worldwide for over 10 years.
Artificial Intelligence & machine learning have been a part of many of our past QCon's:
Attendees talk about the Speakers
