Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Published on Aug 1, 2017
With AI research and machine learning systems growing at great speed, companies require significant effort to keep up or risk losing their relevance in this brave new world. The new tide also brings with it numerous tools to tackle previously intractable problems. However, there does seem to exist a gulf between appreciating these developments and subsequently deploying them. Despite the global push to democratize machine learning, the steps prescribed don’t align with the fuzzier problems that need solving.
As a startup focused on organizing the world’s e-commerce data, Semantics3 has faced its fair share of challenges. To tackle numerous problems covering categorization, feature extraction, cross-domain product matching and price tracking, we have had to incorporate multiple modern techniques into our workflows.
Going over our experiences, I would like to share the broader questions (not whether you need a CNN, GAN or TROL) that need to be considered. Datasets, frameworks and deployment practices - are just some of the topics I wish to touch upon. The talk is almost a recollection of our journey when moving machine learning from practice to production in an e-commerce-centric environment.