Workshops: April 15, 2019

Conference: April 16-17, 2019

Applied AI for Developers

Practices and use cases for applying AI & machine learning in software engineering

QCon.ai is a dedicated AI and machine learning conference for senior software engineers, architects, and technical managers.

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

2019 Registrations Now Open! Save up to $210 before Jan 12th!

Benefits of attending QCon.ai:

  • Learn how software innovators are applying AI & machine learning in use-case oriented sessions
  • Hear about the tools and techniques of AI & machine learning
  • Go in-depth on key topics in our 1 day of workshops
  • Meet with AI and ML leaders from innovator and early adopter companies
  • Gain valuable insights and ideas to shape your AI and machine learning projects

AI and Machine Learning Software Development Conference
#QConAI

Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches, including image recognition, NLP, predictions, & modeling.

  • Deep Learning in Practice

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe including use cases on machine translation, computer vision, & image recogition.

  • AI Meets the Physical World

    Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.

  • Papers to Production: CS in the Real World

    Groundbreaking papers make real world impact.

  • Solving Software Engineering Problems with Machine Learning

    Anomaly detection, ML in IDE's, bayesian optimization for config. Machine Learning techniques for more effective software engineering.

  • Predictive Architectures in the Real World

    Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.

AI/ML Is What's Next for Software Engineers

Video filmed on March 5th at QCon London 2018

Bleeding-edge for the Enterprise

Bring trends from innovator and early adopter companies home to your team

Apache Beam

Deep Learning

Unsupervised Learning

Self-Driving Vehicles

Transfer Learning

Reinforcement Learning

Machine Learning Model Interpretability

SQL over Streams

Stream Processing

Chatbots

Recommendation Engines

Lambda/Serverless

Supervised Learning

Tensorflow

Natural Language Processing

Sequential Data

Demand Modeling

DevOps

Containers

Spark Streaming

Kafka

Jupyter Notebooks

R / Python Use Cases & Tips

Last Year's Keynotes

Inside a Self-Driving Uber

Over the course of three years, Uber’s self-driving vehicles have driven over 2 million miles and have completed over 50,000 passenger trips in Pittsburgh and Phoenix. Many of you might be curious as to how we built a fleet of self-driving vehicles capable of driving autonomously in varying terrains and conditions. In this talk, Matt will break down the software components that come together to make a self-driving Uber drive itself. You’ll also learn about how we thoroughly test new software before it is deployed to the fleet.

Sr. Staff Engineer @UberATG

Analyzing & Preventing Unconscious Bias in Machine Learning

Increasingly AI is finding its way into nearly every product we use (everything from photo sharing apps to criminal justice decision algorithms), but often various types of bias are buried in the underlying data and models.  This can have a damaging impact on both individuals and society. Through the lens of 3 case studies, we will look at how to diagnose bias, identify some sources, and take steps to avoid it.

fast.ai founder & USF assistant professor

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

 

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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