8 Data Conferences Shopify Data Thinks You Should Attend

Summary

Shopify’s data scientists and engineers curated eight upcoming data conferences for 2022, covering virtual, hybrid, and in-person formats. The selection supports learning and development by helping practitioners hear from peers about applications, techniques, use cases, and opportunities in the wider data community. Hybrid options include Data + AI Summit, Transform, RecSys, and ODSC West, with topics spanning analytics, machine learning, open source technologies, recommender systems, MLOps, natural language processing, and big data. In-person choices—PyData London, KDD, Re-Work Deep Learning Summit, and Crunch—feature tutorials, research, business applications, AI challenges, data-team practices, and machine learning at scale, with locations including London, Washington D.C., Toronto, and Budapest.

Context

Shopify frames conferences as part of ongoing learning and development, providing ways to broaden perspectives, learn from peers about data science applications and techniques, network, and participate in the wider data community.

Approach / What changed

The article presents eight 2022 conferences curated by Shopify data scientists and engineers, grouping them into hybrid and in-person events and describing each event’s dates, location, subject areas, formats, and learning or networking opportunities.

Takeaways

• Data + AI Summit 2022 combines keynotes, technical sessions, hands-on training, and networking, with coverage of business intelligence, analytics, machine learning, Presto, Looker, Kedro, and open source technologies such as Spark.
• RecSys 2022 includes separate research and industry tracks and focuses on recommender-system applications, reinforcement learning, evaluation and metrics, and bias and fairness across areas including fashion, ecommerce, and media.
• PyData London begins with tutorials on topics such as data validation and training object detection with small datasets, followed by talks on practical applications including Bayesian modeling for real-world business problems.

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