The key skills every software engineer needs in 2018? AI and Machine Learning.

Last year, we saw more and more software engineering teams moving into the AI and machine learning space. For 2018, AI and machine learning are key skill sets that engineers need to focus on. As Sander Mak, Sr Software Engineer at Luminis Technology, author of Modular Java Development in Action, and Speaker at QCon London 2018 put it:

"Machine learning is one of the trends that needs your attention. We've all heard about deep learning and the cool stuff Google is doing with it, but I think that enterprise application 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."

Learn practical AI and machine learning skills
Building these new skill sets is a core focus and aim for our QCon.ai conference. QCon.ai is a complete conference dedicated to machine learning, artificial intelligence, and data engineering but geared towards the software engineer.

With over 30 technical sessions across 2 days, QCon.ai talks range from shorter breadth focused talks to longer case study oriented ones. QCon.ai features six tracks, including an entire track devoted to what IEEE recently highlighted as its top trend for 2018: Deep Learning Application & Practices.

Track focus: Deep Learning Application & Practice
The deep learning track is hosted by Coinbase Director of Data Science Supranamaya Ranjan and will focus on applied lessons leveraging deep learning in the machine learning space. Brad Klingenberg (VP Data Science @StitchFix) leads two half-day tracks on sequential data and ML applied to operations.

Examples of the talks you'll find at QCon.ai include:

• Self-Driving Car Case Study: AI Meets the Physical World by Anthony Navarro (Product Lead Robotics and Self Driving Car @Udacity) / Jendrik Joerdening (Data Scientist @Akka Germany)
• Interpretability of Machine Learning Models by Mike Lee Williams (Director of Research @FastForwardLabs)
• Data Pipelines for Real-Time Fraud Prevention at Scale by Mikhail Kourjanski (Lead Data Architect @PayPal) / Gurinder Grewal (Risk Chief Architect @Paypal)
• Counterfactual Evaluation of Machine Learning Models by Michael Manapat (Head of Conversion Products @Stripe)
• The Black Swan of Perfectly Interpretable Models by Leah McGuire (Principal Member of Technical Staff) and Mayukh Bhaowal (Director of Product Management) of Salesforce
• End-to-End ML Without a Data Scientist by Holden Karau of Google
• Plus many more hand-selected talks

Workshops, Panels, and AMAs
Our talks are supported by workshops, panel discussions with innovators, keynotes from thought leaders, and AMAs with recognized engineers in the space.

NEW: Codelabs - get hands-on with the code
We're introducing a new learning experience and skill development option at QCon.ai: codelabs. Codelabs are a combination of self-paced tutorials followed by office hours with the creators. Codelabs create an opportunity to work hands-on with AI tooling followed by the opportunity to get your questions answered, unblock on a tricky bit of code from the lab, or simply chat about your own use case with the lab's creators.

QCon.ai takes place in San Francisco from April 9-11, 2018. You can find out more about the conference, the most up to date agenda and list of speakers at QCon.ai. Should any of your colleagues express interest in attending as well, we offer discounts for groups as small as three people. Drop us a note at info@qcon.ai if you wish to receive more information. We hope you see you there.

PS: Check out some of the recent talks at QCon that demonstrate the basis of what we're diving deeper into at QCon.ai. You can also follow the machine learning topic on InfoQ to get notifications about the latest trends in the area.

Leslie Miley Engineering Leadership, previously of Slack, Google, & Apple Bias in Big Data/AI and ML: Leslie Miley discusses how inherent bias in data sets have affected things from the 2016 Presidential race to criminal sentencing in the United States. He shares lessons on the past heritage and how forward-looking companies are developing mitigation strategies to prevent bias in their systems.

Nikhil Dandekar Leads NLP Team @Quora ML for Question and Answer Understanding at Quora: Nikhil Dandekar discusses how Quora extracts intelligence from questions using machine learning, including question-topic labeling, removing duplicate questions, ranking questions & answers, and more. He highlights lessons he has learned from applying state-of-the-art machine learning techniques to consumer products at scale.

Sincerely,
The QCon team

You have received this email because you opted-in to the ''InfoQ Industry Email Notices'' box when you registered to InfoQ.com, where we send infrequent notices on behalf of our partners, user groups, conferences, etc.

To unsubscribe to the InfoQ Industry Email Notices, please click the following link: Unsubscribe.