Recently, the people behind QCon (InfoQ’s conference for senior developers, architects, and leaders in software) announced a new conference called QCon.ai.
Designed as a software conference focused on the practices of artificial intelligence and machine learning, QCon.ai takes a different tack than other major software conferences in the AI/ML space do today. Rather than focusing on data scientists, QCon.ai targets how full-stack software engineers can apply machine learning and AI techniques and toolkits in their day-to-day roles.
Today QCon.ai’s program committee, which includes: Supranamaya (Soups) Ranjan (Director of Data Science @Coinbase), Brad Klingenberg (VP of Data Science @Stitch Fix), Roland Meertens (Machine Learning and Computer Vision Research Engineer), and Sid Anand (Chief Data Engineer @PayPal) together with Charles Humble (InfoQ's Chief Editor) and Wes Reisz (QCon's conference chair and host of The InfoQ Podcast), announced the tracks (or topics) comprising the first annual QCon.ai software conference.
The QCon.ai tracks announced today focus upon:
- Deep Learning Applications & Practices: Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision
- 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
- Real-World Data Engineering: Showcasing DataEng tech and highlighting the strengths of each in real-world applications
- ML Applied to Operations (Half-Day Track): Machine learning in the data center. Exploring topics like dynamic rebalancing in dataflow, predictive auto-scaling, and fault prediction
- AI Meets the Physical World: AI use cases from drones to self-driving cars. The track where AI touches the physical world
- Handling Sequential Data Like an Expert (Half-Day Track): Discussing the complexities of time, including HyperLogLog, count-min sketch, and more
- ML in Action: Security & Risk: Applied machine learning track demonstrating how to train, score, and handle security and fraud use cases
Each of these tracks will combine multiple formats of both long and short form talks (currently proposed to be four of each type per track). As Charles Humble points out, the goal of QCon.ai is really to provide “a practical, real-world event for software engineers, architects and technical managers who want to learn about the successes and use-cases for AI.” The mixed form format is designed to give depth and breadth to the tools and practices of machine learning.
QCon.ai is a two-day AI, machine learning, and data engineering conference designed specifically for software engineers, architects, and technical managers. The conference and optional day of workshops are for technical engineers who want to uncover the real-world use cases for applying artificial intelligence and machine learning in engineering, but who may not come from a data science background.
QCon.ai is organized by the same team behind QCon San Francisco and provides a platform for innovators and early adopters to share their stories in hotbeds of software development.
Registration is $1,495 USD ($300 off the full conference price) for the three-day conference if you register before Nov 17th. Register now to receive the best rate available for this new conference from QCon!