Workshops: April 15, 2019

Conference: April 16-17, 2019

RegisterSave up to $105 before Feb 23rd.

Applied AI Software Conference for Developers

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

Register Save up to $105 before Feb 23rd.

Benefits of attending

  • Learn from speakers driving innovation and change in AI and ML.
  • Identify best practices from those working on in-production projects.
  • Uncover emerging trends and tools.
  • Focus on patterns & practices, not products or pitches.
  • Acquire implementable ideas for your projects.
  • Meet AI and ML leaders from innovator and early adopter companies
  • Validate your AI and ML roadmap.
  • Emphasis on software engineering rather than data science.

2019 Tracks


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

Featured Presentation

Applying Deep Learning To Airbnb Search

Searching for homes is the primary mechanism guests use to find the place they want to book at Airbnb. The goal of search ranking is to find guests the best possible options while rewarding the most deserving hosts. Ranking at Airbnb is a quest to understand the needs of the guests and the quality of the hosts to strike the best match possible. Applying machine learning to this challenge is one of the biggest success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This talk discusses the work done in applying neural networks in an attempt to break out of that plateau. The talk focuses on the elements we found useful in applying neural networks to a real life product. To other teams embarking on similar journeys, we hope this account of our struggles and triumphs will provide some useful pointers. Bon voyage!

Machine Learning Engineer @Airbnb

Testimonials 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.



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


Stay at the venue

Special rate for attendees
(Possibility of early sell-out)

Book your room now


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


Recommendation Engines


Recommendations / Personalization / Relevance



Sequential Data

Fairness / Privacy



Spark Streaming


Jupyter Notebooks

R / Python Use Cases & Tips


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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