Conference: April 10-11, 2018

Workshops: April 9, 2018

Applied AI for Developers

Practices and use cases for applying AI & machine learning in software engineering is a dedicated AI and machine learning conference for senior software engineers, architects, and technical managers.

April 9 - 11, 2018
Parc 55 - A Hilton Hotel, San Francisco, CA

Sign up to be informed when registrations for the next open!

Benefits of attending

AI and Machine Learning Software Development Conference


  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

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


Recommendation Engines


Supervised Learning


Natural Language Processing

Sequential Data

Demand Modeling



Spark Streaming


Jupyter Notebooks

R / Python Use Cases & Tips


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. founder & USF assistant professor

Panel: Building a Data Science Capability (Live Recording of The InfoQ Podcast)

What does it take to build a data science capability? What are the new roles that are being defined in a data-driven company? What do you look for in hiring a data science leader? In this panel discussion, Charles Humble (the Chief Editor of InfoQ) and Wes Reisz (Host of The InfoQ Podcast) record a live session with the organizing committee for to discuss these questions. Curious how to understand what they're looking for in new hires and the leaders they promote? Come learn with us in this bookend to day 1 of

Director of Data Science @Coinbase
Assistant Professor of Computer Science @Stanford and Chief Technologist @Databricks
Data Scientist @StitchFix
Chief Data Engineer @PayPal
Software/Technical Advisor C4Media & QCon Chair, previous Architect @HPE


Sponsored by IBM

Guided, hands-on coding experience

Codelabs are self-guided tutorials for a technology or set of tools. You do not have to be an attendee to work through one of the codelabs. If you are an attendee, you'll notice that we have office hours with the authors. Codelabs at give you the opportunity to get your hands on with the tools of AI.

Machine Learning

50 min

Tensorflow without a PhD Codelab Speaker Office Hours

Codelab Office Hours: 2:45pm on Tuesday


50 min

[CANCELED] Docker-izing your Data Science Applications Codelab

Codelab Office Hours: 1:10pm on Tuesday


50 min

TensorBoard Codelab Speaker Office Hours

Codelab Office Hours: 1:05pm on Wednesday

The People Behind The Conference

Speakers for are invited by our committee of industry practitioners. This is our amazing committee
Director of Data Science @Coinbase
Soups Ranjan

Soups Ranjan is the Director of Data Science at Coinbase, one the largest bitcoin exchanges in the world. He manages the Risk & Data Science team that is chartered with preventing avoidable losses to the company due to payment fraud or account takeovers.

VP Data Science @StitchFix
Brad Klingenberg

Brad Klingenberg leads a team of 20+ data scientists working on human-in-the-loop machine learning at Stitch Fix. His team develop the recommendation algorithms that guide our stylists, the human experts who curate the items selected for clients. We also match our clients and stylists together and measure, monitor and optimize the role of human selection in our recommendation system.

Chief Data Engineer @PayPal
Sid Anand

Sid Anand currently serves as PayPal's Chief Data Engineer, focusing on ways to realize the value of data. Prior to joining PayPal, he held several positions including Agari's Data Architect, a Technical Lead in Search @ LinkedIn, Netflix’s Cloud Data Architect, Etsy’s VP of Engineering, and several technical roles at eBay.

Machine Learning Engineer @Autonomous Intelligent Driving
Roland Meertens

Roland Meertens is Machine Learning Engineer at Autonomous Intelligent Driving. He works on the machine learning side of the perception software stack that will be deployed to the autonomous vehicles that will soon roam urban environments in Germany.


Why Senior Developers, Architects, and Software Engineers are looking for AI and Machine Learning Topics geared towards them? 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, 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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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.



Parc 55 San Francisco – a Hilton Hotel
55 Cyril Magnin Street
San Francisco, California
94102 USA
Tel: +1-415-392-8000


Book your room at Parc 55 now:

On the hotel website

or call +1-415-392-8000

(Possibility of early sell-out)