InfoQ believes that knowledge of machine learning and other AI techniques is fast becoming essential for software developers. In view of this we created a brand new conference, QCon.ai, to help developers acquire the skills and knowledge they need, and have also invested heavily in machine learning and AI content on InfoQ itself.

The inaugural QCon.ai took place in San Francisco on April 9-11. It was attended by 270 senior software engineers, architects and technical engineering managers.

The event featured real-world case studies from organisations including Google, Udacity, PayPal, Coinbase, Stitch Fix, Uber and LinkedIn that showcased how software engineers can apply machine learning and AI techniques and toolkits in their day-to-day roles. It included both short 10 minute intro talks and longer 50 minute detailed case study talks, as well as code labs and workshops to provide both hands-on and lecture-style learning.

In this special newsletter we bring you up to date on content that we recently filmed at the show, as well as our top AI, ML, and Data Engineering content on InfoQ.

“QCon.ai is more than merely a conference showcasing bleeding-edge technology. It links engineering problems to business problems with speakers that share real-world, working technologies and solutions in the emerging-to-early adopter quadrant.” - Jim Shedlick, Director of Architecture @LabelInsight

QCon.ai 2018 Presentations and Podcasts

Building a Data Science Capability In this podcast, recorded live at QCon.ai, Wes Reisz and Charles Humble chair a panel discussion with Stephane Yee, Matel Zaharia, Sid Anand, and Soups Ranjan.

End-to-End ML without a Data Scientist Holden Karau discusses how to train models, and how to serve them, including basic validation techniques, A/B tests, and the importance of keeping models up-to-date.

Machine Learning Pipeline for Real-Time Forecasting @Uber Marketplace Chong Sun and Danny Yuan discuss how Uber is using ML to improve their forecasting models, the architecture of their ML platform, and lessons learned running it in production.

Counterfactual Evaluation of Machine Learning Models Michael Manapat discusses how Stripe evaluates and trains their machine learning models to fight fraud.

Developing Data and ML Pipelines at Stitch Fix Jeff Magnusson discusses thoughts and guidelines on how Stitch Fix develops, schedules, and maintains their data and ML pipelines.

Counting is Hard: Probabilistic Algorithms for View Counting at Reddit Krishnan Chandra explains the challenges of building a view counting system at scale, and how Reddit used probabilistic counting algorithms to make scaling easier.

The Black Swan of Perfectly Interpretable Models Mayukh Bhaowal, Leah McGuire discuss how Salesforce Einstein made ML more transparent and less of a black box, and how they managed to drive wider adoption of ML.

QCon.ai 2018 Short Talks

• Introduction to Forecasting in Machine Learning and Deep Learning
• NVIDIA Jetson
• Two Effective Algorithms for Time Series Forecasting
• When do you use Machine Learning vs. a Rules Based System?
• PyTorch by Example
• Tooling and Setup for My Neural network

“QCon.ai featured high quality talks from the innovators in the industry with no sales pitch. It helped me to get a much broader and deeper understanding on where AI is headed and some of the areas where it is actively applied.” - Prathima Donapudi, Senior Software Engineer @Netflix

All full-length presentations recorded at QCon.ai will be made available here during the coming weeks. You will then be able to share it with your friends or peers. To make sure you won’t miss the most important ones you can:

• Follow the Qcon.ai topic on InfoQ
• Subscribe to the InfoQ Youtube channel and save the AI, ML and Data Engineering playlist.

Top Viewed Content on InfoQ

• Google Brings Machine Learning to Firebase with ML Kit
• Tensorflow with Javascript Brings Deep Learning to the Browser
• How Booking.com Uses Kubernetes for Machine Learning
• Facebook Releases Open Source "Detectron" Deep-Learning Library for Object Detection

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