Workshops: April 15, 2019

Conference: April 16-17, 2019

The Applied AI Software Conference for Developers

Uncover real-world practices and use-cases for AI and machine learning in software development

QCon.ai is a practical AI and machine learning conference bringing together software teams working on all aspects of AI and machine learning.

Join us to discover emerging AI trends, essential tools, and learnings to validate your software roadmap.

April 15 - 17, 2019
Parc 55 - A Hilton Hotel, San Francisco, CA

Benefits of attending QCon.ai:

2019 Tracks

  • 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.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

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

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

Natural Language Processing by svgsilh.com is licensed under CC BY 2.0.

"Transformers"? "BERT"? "ELMo"? What do NLP researchers actually do other than give things childish names? In this talk I'll discuss the kinds of problems NLP researchers are thinking about, explain their newest and hottest breakthroughs, and give you some ideas as to how you can incorporate these concepts and models into your work.

Senior Research Engineer @allen_ai

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.

Senior Cloud & Big Data Developer @PayPal
Big Data Developer @PayPal

Testimonials

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)

Book your room now

 

Is QCon.ai Right for You?

Our attendees roles are:

Software Developer / Programmer/ Engineer

Senior Developer / Engineer

Technical Team Lead and Higher (including):

  • Technical Team Lead

  • Senior Management (VP, CTO, CIO, Director)

  • Architect: Technical / Application (platform specific)

  • Enterprise Architect / Chief Architect

  • Architect: Solution / Systems

  • 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.

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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