Case Studies + Research

Case Studies + Research

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This is a new research series focusing on increasing the discoverability of your products and content, by combining deep learning APIs into Elasticsearch.

The brain of e-commerce: Search and Recommendations

Search is core to marketplace and e-commerce businesses, guiding visitors into becoming customers, or matching buyers to sellers. Many of our clients use Elasticsearch as a backbone to their search engine. One of the biggest challenges is populating product listings to a level that enables search to perform well.

By mapping unstructured data (images, descriptions, etc.) in your product listings into structured information that Elasticsearch understands, we can scale your marketplace faster and cheaper. Using deep learning to remove the need for manual profile curation and enrichment, you can match demand and supply more accurately and more consistently.

Deep learning models are expensive to train from scratch, and MLaaS offers a solution

The core benefit of pairing Elasticsearch with managed Machine Learning as a Service (MLaaS) is that you can later substitute the 3rd party service with your own model, trained on your in-house data. The interface to Elasticsearch will remain the same. Give us a shout, and we would happily chat about how we can make that happen.

The power of deep learning on unstructured data like images, text, audio, and video captures everybody’s imagination, and now with the proliferation of APIs, businesses have a cheap way to see whether this new technology can move the needle, before fully committing to a team of data scientists and AI researchers.

Many companies are trying to capitalize on the recent surge in our collective understanding of deep learning, including Google’s Vision and Language APIs, IBM’s Watson cognitive services, and Clarifai’s image and video recognition API.

Executive summary + code samples

Every article starts with a plain English, intuitive summary of what’s going on. Business metrics and case studies will be involved to frame the value. It then dives in to show working, prototypical code that you can use to do it yourself. I hope you enjoy this series, please comment and give feedback!

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